{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000001.SZ   20241225  11.86  12.02  11.84  11.92      11.86    0.06   \n",
      "1     000001.SZ   20241224  11.72  11.87  11.72  11.86      11.73    0.13   \n",
      "2     000001.SZ   20241223  11.64  11.84  11.64  11.73      11.62    0.11   \n",
      "3     000001.SZ   20241220  11.59  11.70  11.58  11.62      11.59    0.03   \n",
      "4     000001.SZ   20241219  11.59  11.64  11.54  11.59      11.65   -0.06   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3564  000001.SZ   20100108  22.50  22.75  22.35  22.60      22.65   -0.05   \n",
      "3565  000001.SZ   20100107  22.90  23.05  22.40  22.65      22.90   -0.25   \n",
      "3566  000001.SZ   20100106  23.25  23.25  22.72  22.90      23.30   -0.40   \n",
      "3567  000001.SZ   20100105  23.75  23.90  22.75  23.30      23.71   -0.41   \n",
      "3568  000001.SZ   20100104  24.52  24.58  23.68  23.71      24.37   -0.66   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0      0.5059  1475282.94  1.759957e+06  \n",
      "1      1.1083  1350836.91  1.595699e+06  \n",
      "2      0.9466  1659404.76  1.953519e+06  \n",
      "3      0.2588   714646.27  8.314375e+05  \n",
      "4     -0.5150   697379.04  8.084657e+05  \n",
      "...       ...         ...           ...  \n",
      "3564  -0.2200   288543.06  6.506674e+05  \n",
      "3565  -1.0900   355336.85  8.041663e+05  \n",
      "3566  -1.7200   412143.13  9.444537e+05  \n",
      "3567  -1.7300   556499.82  1.293477e+06  \n",
      "3568  -2.7100   241922.76  5.802495e+05  \n",
      "\n",
      "[3569 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('3a25b97d074af521d24c55e3c29aceff31b1c2ab41edec5f1f1a4163')\n",
    "# 拉取数据\n",
    "df = pro.daily(\n",
    "    ts_code=\"000001.sz\",\n",
    "    start_date=20100101,\n",
    "    end_date=20241225,\n",
    "    fields=[\n",
    "        \"ts_code\",\n",
    "        \"trade_date\",\n",
    "        \"open\",\n",
    "        \"high\",\n",
    "        \"low\",\n",
    "        \"close\",\n",
    "        \"pre_close\",\n",
    "        \"change\",\n",
    "        \"pct_chg\",\n",
    "        \"vol\",\n",
    "        \"amount\"\n",
    "    ]\n",
    ")\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000001.SZ   20241225  11.86  12.02  11.84  11.92      11.86    0.06   \n",
      "1     000001.SZ   20241224  11.72  11.87  11.72  11.86      11.73    0.13   \n",
      "2     000001.SZ   20241223  11.64  11.84  11.64  11.73      11.62    0.11   \n",
      "3     000001.SZ   20241220  11.59  11.70  11.58  11.62      11.59    0.03   \n",
      "4     000001.SZ   20241219  11.59  11.64  11.54  11.59      11.65   -0.06   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3564  000001.SZ   20100108  22.50  22.75  22.35  22.60      22.65   -0.05   \n",
      "3565  000001.SZ   20100107  22.90  23.05  22.40  22.65      22.90   -0.25   \n",
      "3566  000001.SZ   20100106  23.25  23.25  22.72  22.90      23.30   -0.40   \n",
      "3567  000001.SZ   20100105  23.75  23.90  22.75  23.30      23.71   -0.41   \n",
      "3568  000001.SZ   20100104  24.52  24.58  23.68  23.71      24.37   -0.66   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0      0.5059  1475282.94  1.759957e+06  \n",
      "1      1.1083  1350836.91  1.595699e+06  \n",
      "2      0.9466  1659404.76  1.953519e+06  \n",
      "3      0.2588   714646.27  8.314375e+05  \n",
      "4     -0.5150   697379.04  8.084657e+05  \n",
      "...       ...         ...           ...  \n",
      "3564  -0.2200   288543.06  6.506674e+05  \n",
      "3565  -1.0900   355336.85  8.041663e+05  \n",
      "3566  -1.7200   412143.13  9.444537e+05  \n",
      "3567  -1.7300   556499.82  1.293477e+06  \n",
      "3568  -2.7100   241922.76  5.802495e+05  \n",
      "\n",
      "[3569 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='万科A'\n",
    "stockcode='000002'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3559</th>\n",
       "      <th>3560</th>\n",
       "      <th>3561</th>\n",
       "      <th>3562</th>\n",
       "      <th>3563</th>\n",
       "      <th>3564</th>\n",
       "      <th>3565</th>\n",
       "      <th>3566</th>\n",
       "      <th>3567</th>\n",
       "      <th>3568</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>11.86</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.56</td>\n",
       "      <td>11.79</td>\n",
       "      <td>11.73</td>\n",
       "      <td>...</td>\n",
       "      <td>21.02</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.87</td>\n",
       "      <td>22.57</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.75</td>\n",
       "      <td>24.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>12.02</td>\n",
       "      <td>11.87</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.7</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.74</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.66</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.87</td>\n",
       "      <td>...</td>\n",
       "      <td>21.65</td>\n",
       "      <td>21.16</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.65</td>\n",
       "      <td>23.68</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.05</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.9</td>\n",
       "      <td>24.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>11.84</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.52</td>\n",
       "      <td>11.53</td>\n",
       "      <td>11.56</td>\n",
       "      <td>11.71</td>\n",
       "      <td>...</td>\n",
       "      <td>20.65</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.28</td>\n",
       "      <td>22.35</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.72</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>11.92</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.73</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.53</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.56</td>\n",
       "      <td>11.85</td>\n",
       "      <td>...</td>\n",
       "      <td>21.43</td>\n",
       "      <td>20.97</td>\n",
       "      <td>20.96</td>\n",
       "      <td>22.45</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.65</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>11.86</td>\n",
       "      <td>11.73</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.53</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.56</td>\n",
       "      <td>11.85</td>\n",
       "      <td>11.73</td>\n",
       "      <td>...</td>\n",
       "      <td>20.97</td>\n",
       "      <td>20.96</td>\n",
       "      <td>22.45</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.65</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.71</td>\n",
       "      <td>24.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>0.5059</td>\n",
       "      <td>1.1083</td>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.2588</td>\n",
       "      <td>-0.515</td>\n",
       "      <td>1.0408</td>\n",
       "      <td>-0.3457</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>-2.4473</td>\n",
       "      <td>1.023</td>\n",
       "      <td>...</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-6.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>1475282.94</td>\n",
       "      <td>1350836.91</td>\n",
       "      <td>1659404.76</td>\n",
       "      <td>714646.27</td>\n",
       "      <td>697379.04</td>\n",
       "      <td>1016589.66</td>\n",
       "      <td>802119.95</td>\n",
       "      <td>805717.78</td>\n",
       "      <td>1343792.89</td>\n",
       "      <td>986234.59</td>\n",
       "      <td>...</td>\n",
       "      <td>539508.46</td>\n",
       "      <td>521194.74</td>\n",
       "      <td>935039.47</td>\n",
       "      <td>591795.91</td>\n",
       "      <td>442846.02</td>\n",
       "      <td>288543.06</td>\n",
       "      <td>355336.85</td>\n",
       "      <td>412143.13</td>\n",
       "      <td>556499.82</td>\n",
       "      <td>241922.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1759956.63</td>\n",
       "      <td>1595698.79</td>\n",
       "      <td>1953519.499</td>\n",
       "      <td>831437.46</td>\n",
       "      <td>808465.664</td>\n",
       "      <td>1186285.805</td>\n",
       "      <td>929225.455</td>\n",
       "      <td>934226.189</td>\n",
       "      <td>1565589.089</td>\n",
       "      <td>1164062.448</td>\n",
       "      <td>...</td>\n",
       "      <td>1146375.3212</td>\n",
       "      <td>1089329.4538</td>\n",
       "      <td>1990492.7403</td>\n",
       "      <td>1310068.9938</td>\n",
       "      <td>1009985.5665</td>\n",
       "      <td>650667.4049</td>\n",
       "      <td>804166.3158</td>\n",
       "      <td>944453.6973</td>\n",
       "      <td>1293476.9391</td>\n",
       "      <td>580249.4715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3569 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0           1            2          3           4     \\\n",
       "ts_code      000001.SZ   000001.SZ    000001.SZ  000001.SZ   000001.SZ   \n",
       "trade_date    20241225    20241224     20241223   20241220    20241219   \n",
       "open             11.86       11.72        11.64      11.59       11.59   \n",
       "high             12.02       11.87        11.84       11.7       11.64   \n",
       "low              11.84       11.72        11.64      11.58       11.54   \n",
       "close            11.92       11.86        11.73      11.62       11.59   \n",
       "pre_close        11.86       11.73        11.62      11.59       11.65   \n",
       "change            0.06        0.13         0.11       0.03       -0.06   \n",
       "pct_chg         0.5059      1.1083       0.9466     0.2588      -0.515   \n",
       "vol         1475282.94  1350836.91   1659404.76  714646.27   697379.04   \n",
       "amount      1759956.63  1595698.79  1953519.499  831437.46  808465.664   \n",
       "\n",
       "                   5           6           7            8            9     \\\n",
       "ts_code       000001.SZ   000001.SZ   000001.SZ    000001.SZ    000001.SZ   \n",
       "trade_date     20241218    20241217    20241216     20241213     20241212   \n",
       "open              11.58       11.57       11.56        11.79        11.73   \n",
       "high              11.74       11.65       11.66         11.8        11.87   \n",
       "low               11.57       11.52       11.53        11.56        11.71   \n",
       "close             11.65       11.53       11.57        11.56        11.85   \n",
       "pre_close         11.53       11.57       11.56        11.85        11.73   \n",
       "change             0.12       -0.04        0.01        -0.29         0.12   \n",
       "pct_chg          1.0408     -0.3457      0.0865      -2.4473        1.023   \n",
       "vol          1016589.66   802119.95   805717.78   1343792.89    986234.59   \n",
       "amount      1186285.805  929225.455  934226.189  1565589.089  1164062.448   \n",
       "\n",
       "            ...          3559          3560          3561          3562  \\\n",
       "ts_code     ...     000001.SZ     000001.SZ     000001.SZ     000001.SZ   \n",
       "trade_date  ...      20100115      20100114      20100113      20100112   \n",
       "open        ...         21.02          21.0         21.87         22.57   \n",
       "high        ...         21.65         21.16          21.9         22.65   \n",
       "low         ...         20.65          20.6          20.8          21.8   \n",
       "close       ...         21.43         20.97         20.96         22.45   \n",
       "pre_close   ...         20.97         20.96         22.45          22.6   \n",
       "change      ...          0.46          0.01         -1.49         -0.15   \n",
       "pct_chg     ...          2.19          0.05         -6.64         -0.66   \n",
       "vol         ...     539508.46     521194.74     935039.47     591795.91   \n",
       "amount      ...  1146375.3212  1089329.4538  1990492.7403  1310068.9938   \n",
       "\n",
       "                    3563         3564         3565         3566          3567  \\\n",
       "ts_code        000001.SZ    000001.SZ    000001.SZ    000001.SZ     000001.SZ   \n",
       "trade_date      20100111     20100108     20100107     20100106      20100105   \n",
       "open                23.5         22.5         22.9        23.25         23.75   \n",
       "high               23.68        22.75        23.05        23.25          23.9   \n",
       "low                22.28        22.35         22.4        22.72         22.75   \n",
       "close               22.6         22.6        22.65         22.9          23.3   \n",
       "pre_close           22.6        22.65         22.9         23.3         23.71   \n",
       "change               0.0        -0.05        -0.25         -0.4         -0.41   \n",
       "pct_chg              0.0        -0.22        -1.09        -1.72         -1.73   \n",
       "vol            442846.02    288543.06    355336.85    412143.13     556499.82   \n",
       "amount      1009985.5665  650667.4049  804166.3158  944453.6973  1293476.9391   \n",
       "\n",
       "                   3568  \n",
       "ts_code       000001.SZ  \n",
       "trade_date     20100104  \n",
       "open              24.52  \n",
       "high              24.58  \n",
       "low               23.68  \n",
       "close             23.71  \n",
       "pre_close         24.37  \n",
       "change            -0.66  \n",
       "pct_chg           -2.71  \n",
       "vol           241922.76  \n",
       "amount      580249.4715  \n",
       "\n",
       "[11 rows x 3569 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151da4a31d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FIKKWFlHGxSSn71l2y6LeSaMQwUC1exgoKf7aRqrIawZq7p7DFfVhbAlBEHrxiETK5xvLhM98hgZKyEiEAokUJmyVcszVEOZ2xAdOldlUdU0kDAnCyogtKY0tPusJL15Y7p65N58VjqYRMQCJFB00yCryjureJkItiS3U3D05qYlhbAlBEHrxKBTYanZ7sO9kjchl61MpF/ZrF4aWEbEAiRQGSu4efvmM83sCALq3TQtTi2Ibp0P5Z0iGYoKwNl3apDKXLyk5jvNfXIlDp70uWwqcJYKBRAoDpWeJn0LHj/ypQq8xUMExgoheOmWnAAhsHaGnnAgGEikMlObzC6XGW1+qFBBmPnSLCSI66N0uQzIFmSCMgESKDuTahabHGgPdRoKIXvh+MMEGPHPlAMXtKJkbEQwkUhgEepQSbLwlxfy2EHSTCcLKeIQCgjakJSqn3iKJQgQDiRQGioGzrY8ZH0JBMSnGQEUECSJ64YS09+rWEvmq7nneiQdnds0xq2lEDEAiRQf8Q+azpNDL1QjUbiPdYoKwNvxgLcFmg54Y+HvGFwEAklRm9xEE/ToY2BQMk3zArE2wpNAb1AjoLhJE9MKJYlLUwk7k/Spvddl7kqrJE8qQSGGg9KDxmVF5Swq5e8xHfIuXlRzHb177GQdO1UasPQRBSPG5e2yq7p4E2duGt7ocdzVizb5TJrWOiHZIpOjA3vqUkSXFWLS6zab+Zz3WH6jAfZ8Um9wigiC04gucVQ+Otct8QQkiQfO/4iNmNI2IAUik6OC64QUARDEpkWxMDKF2H1n65XRtk2ltIQhCH76k9zaJ8JBjl60TfyOrNKGE8nyxOEb+nGUkObD43nOFzIr8gIACZw1C5210U49GEJbBozEmJSGBHZMCUF9KKEOWFA14OE4QKIDv4fJ4lPYg9NDkVr6RrOnJ5GYjCOvAiWb3uOqVq5bLLSlizULPNKEEiRQNyF+ivsBZerCM4J/Ld+vanm47QVgHThSTopbzSB6TIrakqIxTiDiHRAoD+UtQ7l2gZG7GsrXMpbiOJUhIHBKEdfCIZvdM6N9ecTu5u0f8tZlUCqEAiRQGgd6BNopJMRV7gk01wVMLqUOCsAzimBSnPQF3juvB3M7f3eP7XlFHwfAEGxIpDOQmy3G920q+22h2j6nYE2wY2sWbKpt1jxua3eFtEEEQivhm96gjz0abnuybt3GyutHQNhGxA83uYSA2kNw1rid+f06hZD3FpJgLa5aAeEZPdYNycB5BEOFFyDgbICe+fH37zGThMz3ThBIkUhiIxced43oiJdEuWU8xKeYiTp/Nd4Ard52IVHMIglBBnHFWDbm7p51IpJyqJUsKwYbcPQzE4kOeyhmgAoNG0zknRfLdxrCk1DRKXTx07wnCGggZZ1u/Kz2acktKoijurKGZAmcJNiRSmPieMlYGRUqLbyyXDe4o+c66580t0k6Mbj1BWINvtx0HwH5uxcinIBOEFkikMBC/ANkihZK5GYlccNjgXzG1RXazSSAShLUoLfdWM1bSKnJ3D0FogUQKg6L8DCQ6EpBg849IB0Rp8Wl+jyHIXTecZJ3332a3dBuKByIIa3EqQE0tluucIAJBgbMMslKdWPHn85DitDODwXyze8LdsthEbhWpaWzxG421uMmSQhBWhrd+Kj2aZEkhgoFEigLiWj1yqMCgsaiJPd5aJU/gRreeIKxFIA0SKGaFIFiQAS4IbGRJMRQtVhF/dw/dfIKIJgLlUSEIFiRSgoCSuRmL2m3k15G7hyCsTSgaJC890biGEDEFiZQg4J9FsqQYA8ttJo8FavZQ4CxBWA3Ws6vn0XxwYh8AQJ/2mQa1iIg1SKQEgRClTqN5Q9AiOBpl9Xo8pFIIIuKIH0N52gAtdGpN5Oj2cHh28Q48sKCYYv0ICRQ4GwT8KL/4cBW2HK7CwM5ZEW5RdKPmuuFX1TXJRAp1ZAQRccTPYTBxsQmixJivfb8XALD/VB1GFubivot6G9FEIsohS0oQiKPUL/vXqgi2JDZgGUXk/Z2/SDGvPQRBaEM8VuC7RT1axc6I71tXehr/WL6HLCoEABIpQUFB6sbBcRw+XHdQcf3RqnoAQHaq028/giAiS6gWTX7Gj5sx6mhsoZTeBImUoKD5/sZxsppd/XTnsWoAwAtLdgEAHDJlSJYUgog8UktKazI3HfvzlhR5HiQAaKSigwRIpAQFSRTjUOrQjrkahM8nqxvxxg/7JOspJoUgIg/rOWyXkSR8fmCielxJapIdgDfLtJyGFrffMiL+oMDZIGClyifM42+LSvyWkUghiMjjFj2HvLHz+hFdseNYNc7r3RaHK+pV909N9L6C6pv8BQlZUgiALClBQTEp4WXhr0f8lpFGIYjI0yyKG3Hava+TREcCZl81CBMHdAg4oOPdPfKM0gBZUggvJFKCgNI7G0ewd5IsKQQReVjiQkxhXqrqersQOOtvNWloJpFCkLsnKEijRB4KnCWIyNMsKlfBGjeM652PRy7ti34d2RllHfbWwFmG2KHZPQQQhCVl4cKF6N69OxwOB4YMGYLt27cDALZu3Yphw4YhJycH999/v+YpogsWLEDXrl3RsWNHfPjhh3qbExHkJkyaDhs8wd45sqQQROQRi5SR3XP91ttsNkwd0x2je+Qx9+dnSjYzLCkeD4faxhY8t3gHSo64DGoxEW3oEil79+7FrbfeitmzZ6OsrAy9evXC1KlT0djYiMsuuwxnnnkm1q9fj5KSEsyfPz/g8bZu3YobbrgBjz76KL799ls89thj2LlzZ7DXEjYOna6TfA9k8iSUCVZsUFp8gogcJ6sbwXGcpO+b0L+97uPwqQVYlhQOwNNfb8er3+/F5a/+FHRbiehGl0jZvn07Zs+ejauvvhrt2rXD9OnTsWnTJnzzzTeoqqrCSy+9hB49euCZZ57BvHnzAh7vzTffxLhx4zB16lQMHDgQd911F959992gLyZcNMnMkI0U4BU0wWoNkigEERm+3nIUw55ehke+3CpYUtpnJgc161GISWEVKuSApSXHAfj3uUT8oEukTJo0CbfddpvwfefOnSgqKkJxcTFGjhyJ1FRvkNSgQYNQUuI/bVROcXExzj//fOH78OHDsWHDBsXtGxsb4XK5JH+RQJ7MjXynwaPkKstKcTKX85C7hyAiw3OLdwAA3l97EE2tIsXpCC5Qj+9KWY8zx3EUPEsEP7unqakJL774IqZNmwaXy4XCwkJhnc1mg91uR0VFheox5PtlZmbiyBH/6aY8s2bNQlZWlvBXUFAQbPNDQj5gIJESPEpa4+t7xqjux3BhEwQRBsQWE34KMj/9WC92lVkIHIAkB01AjXeC/gU8/vjjSEtLw9SpU+FwOJCUlCRZn5ycjLq6OoW9vcj3C7TPzJkzUVVVJfwdOnQo2OaHhJ9IIbUfNEoWkU7ZKar7ceTwIYiIs/FgJQBg38naoPZXKzHCcUBikOKHiB2CmoK8fPlyzJkzB2vWrIHT6URubi62bt0q2aa6uhqJiYmqx8nNzcXJkyc175OUlOQnhiKBTZbdg1Uci9CG2q0b1i0Hv+xnW+Ni1dvT7PYEPSoliHAg7v1eXrortGOpeIk4cLDbKd9DvKO7NywtLcV1112HOXPmoF+/fgCAYcOGYfXq1ZJtGhsbkZvrPyVNjHy/TZs2oVOnTnqbFHFi9H0ZFtRiS3LTlAVrLMakrNl3CkUPf4M3f9wXeGOCiBQi3XDP+KKQDmVXUSkejoq5EjpFSn19PSZNmoQpU6bgiiuuQE1NDWpqajBmzBi4XC68/fbbAIBnnnkG48ePh93uLR5VWVkJt9vfJXLVVVfho48+wpYtW1BTU4N//OMfmDBhggGXZS4dspIl32PwfRk21HLMqPmrX/t+rxnNiSh//rQYAPC3Rdsj3BKCUEb8VLZpHUiM75sf1LHU3T2cqogh4gNdImXJkiUoKSnB3LlzkZGRIfyVlZXhzTffxF133YW8vDwsXLgQzz77rLBfTk4OtmzZ4ne8wYMH45577sFZZ52FTp06wW6344477gj9qkxmaNccyXeKjwgeNXePWgf2zdZj2HRQPTCbIAjjERcN9D2+wYkJVZECoLymMajjErGDrpiUKVOmKI58u3Xrhr1792LDhg0YOXIk2rRpI6xTGy0//fTTuOGGG1BWVoaxY8cGjGOxAvIHi2aaBI+a20bNkgIAp2qajG4OQRABEM9m5B/fYEuFJKgMkw9X1MPV0BLcgYmYwdDaPe3bt8ell16qe79+/foJ8S3RgPyBJEtK8KgJPDL1EoS1qKiVDgz4QUawj6qaJWXx1qPBHZSIKWgaQRD41+7Rvm9TiwdT/rUKMz/3d3/FI2oCj6pNE4S1OOZqkHznn175jEetqFlLf9pzSnouCv6LS0ikBMm6hy8Iar81+06h+HAVPlx30OAWRSdq/Q5ZUgjC4oRoSdGzXwuleohLSKQESX5GMjq2zvLRMx3WIRo5UJE89XsXb5YU0mREtCFYUkxw98ipqm8O7iREVEMiJQR4t48eK6T4xRuLuT70oqbTKKcZQVgLeZfFfw/a3aNDpByragi8ERFz0GvAAPRIDfFDzqr8GW+ozu4h0wJBWJrPNx4GEB53z6lams0Xj5BICQF++lywAV2kUdTvXSB3D90+gggv8kD34sNVAIDtR4OrSG+z2TQLFbI8xyckUkKAN3HqCS0RP+T00AVw95AlhSAshVKXFUq8iObnnLrLuIRESgj4nq3gnh6Km1UPHpZPT7z17G6Y2L+98D3O4moJIuIozbCRp2XQg9bgWRrUxSckUkKAf7T0PDuVdb4Rx8tLd+FIZb3K1rGPalp8mQoZ3SMPjS2+GlDpSYbmIow4wQYfEkS4aHGzsy+G8stVyzorhjRKfEIiJQT4EYCeZ2fX8Wrh87xVpfi/+b8Y3KroQrXAoGyEZQPQ0OzrJONtijJBRBolS0oo1YrJkkKoQSIlFFqfLT35TuTP2Y5j1ewN4wT+djjtNnx19zkY2CkL708dAcBfhCQkAA0t/tW0CYIID25FkRL8MbWKFJIo8Uls2cvDjODuiWgroht+dNQzPwMDOmXhf3efI6yTd3w22CiOhyAiiDkxKdq2o7T48QlZUkIgmGRu9JhJ4fs8Vj/lF/VvA5zk4iGIiOE2oeS7VrctaZT4hERKCPDPlq4qyCE+aS1uT0yNKHhLCit4Tt552aBekCzaoRnXhNVpcbP7nlDiRbTHpAR9CiKKIZESAvxsDD3Pp/xB69E2TfO+5TWNGP7Md3jws83aT2hxeMHF6qjkgiTFaYeTcuUTRMRQiklRWq4F7TEppFLiEerxQ4B/tvS5e6Qbt81I0rzvf37ej9O1Tfhk/WHtJ7Q4vPWY5dNOlAmSjtkpcNh928WQQYkgogKlmJTQLCnatiNLSnxCIsUA9Cj8UF6sp+tir3aF4O5hdFRJTunPs31WMvp1yAxHswiCYKAkRkKxpGh14caSm5vQDomUEODNlEp+WhbyLfUk8Kqoi71S5XzfxjL5HjxdJ/nutCfg7vOLwtEsgiAYKIkUJQuLFjS7ezhgz4lq/H3ZblQ3xF5fSLAhkRICe0/WAABu1ZGQTf6Q6wmWbG4xPrI+0nAqlhRXfYvfspREO7rriOMhCMI4FBLO6soVJUd76R4O41/6AS8v24VZ3+wI+nxEdEEiJQQagxENsmdZz2wV8cP82YbYiEvhbwcrJiXJ4ft5fiXKnxKr0OQewuooiRF3OGb3iLrbTQcrgz4fEV2QSDGQFrcHVQFcMvXN0oypaYnB5dP706fFQe1nNXjLEqubcoqCZLu2SQ1TiwiCUEJJjISSPiXQOC2xdbAiPnMMZyIgZJBIMZCeD3+DwX9dgi2HqxS3+c/qA5Lvetw9odTHsCpqMSln98zz244giMihGDhroiWFLyQqPncs50sipJBIMYHL/rVK87bVDf5xF0rEoEbxxaQwfolDCrKFz9QnEUTkUXT3mBiTkpZkBwA0iwJiQknDT0QXVLvHIJqCDGpdtafc4JZEFx6VZG7ZqYmYcX5PwGZDRrLTbz1NSSSI8MKLkQv65OO7HScMOWYgSwrvEn/4i63CMpIo8QOJFIO48c21pp9DPl15f3kturZJjepRhVoyNwC476Lefsui92oJIrrhsy2IkyqGilZ3j5go7vIInZC7xyDW7T8d9nOe98L3mPvjvrCf10jUkrnFG9EsNonY58fdJ/HUVyUAgESH3bDjBnb3+IuUukY3Y0siFiGREmYykkMwXjEe5me+ju58AZxK4Gy8Qe4rwsrcNG+d8NkZJktKgg1Idvq/psprGg07P2FtSKSEgaNV9fhu+3G4PRwGdsqKdHMsBVlSCCL6sBs4qGAFzfOkJTqYIkbPhAMiuqGYFJNpaHZj1KzlAIAnLusXUvroWHyP++5GLF4dQcQmRk4BVrOk2O025noyvMYPZEkJgdTEwH7ZxVuPCZ8/+uVQiFP1Yu/JJEsKQVifRZuPSr4nyB7YW0Z1DfrYav1aZV0zc/xCeVLiBxIpIaAljuKT9YeEzx6OEywpZ/dso/t8oZRDtypqydwCEXt3gyCsyZ0fbJR8l2uERyf1C/rYgfQGazXFsMUPJFJCQOnhunhAewDAwVN1+HnvKWG5hwPcrXNu+URl3fO0F8tLcapbbtweLiRLTSRQS+amRCxalIDYvS4i9pDHpDjs5r1KWM8FWVLiBxIpIaD0oPAP7KcbDvmta2lNNGBP8K9HEQhWlLuY6+auwdjnV6CuKXqCyvgMlvSCJojoQe7uCYVAxQJZp3KQSIkbSKSEgJLJkeO8Fo0FskrFNvgyNjpbHzI9004DGUnWlZ7G4Yp6fLPlmPqGFiIUdw9BEJFBbEnp0z7D1HMlMqw0ZEmJH0ikhIDS6J+DN/HR0aoGZKU4Rdv7RApvbdFjSVETNOJ1p2qjJ4cABc4SRPQhtqR0zkkx9VxJDAsyWVLiBxIpIaDohuV8AbNXnNFJWGyDTQic5ZMh6YmFVSuHLrayRFN87ZHKBgDACVf0CCuCiHcSbDbMu+UsjCnKw98uH2jaee4d3wuJdv9YPLuByeQIa0N5UkJAKaHRqdpGbDhQAQC4+qwCzP95v7BOsKQEMRJQm90jDpiNIo2Ct34qBQCs3ncqwJaxD3W7RLSQYAMu6NsOF/RtZ/p52JYUGl/HC/Q/HQJK7p61pafR7OYwoFMm+nXMFJZnpzrR0moOsQvuntBjUjiOkwiYaLKkEARBKGGzsWNSSstrI9AaIhLoFinl5eUoLCzE/v37AQDz58+HzWbz+5s/f37AYw0aNEiyz9SpU/U2J6IoBW/xIuGaswoAAO0ykwAA/TtmMQJntZ9PSdA0u6Nv6jFP1zapAIDMIGoakRgjiMgQzkePZUkh4gdd//vl5eWYNGmSIFAA4Prrr0dFRYXwd+jQIeTl5WHMmDGqx6qrq8PevXtx4sQJYd9//vOfQV1EpFDz2CQ5EjB5iDce5epWseL2eISYFCFwVo9IUdj23TUH4BZbUqLI4TO2V1sAwO9Gd9O8D7lFCCJ2eOIyXyK4d38/HK/fOFSynmVJAYAWt0qQHhEz6Bq+Xnvttbj++uuxdu1aYVliYiISExOF76+++iquuOIK9OjRQ/VYmzZtwqBBg9C2bVudTY4OLh7QXpjZw1tcWkTJ1oKpIqoUk/LemgO4UhSgG00WBmF2D0XrE0Rc8ruzCzGiexukJzlQkJsqsQrbbDYkKSSx3H2iBn07ZDLXEbGDLkvK3LlzMWPGDMX1DQ0N+Pvf/46//OUvAY+1bt06HD58GG3btkV2djamT5+OxsbomuGhpgV46wngC7D1cD6REsw8fyWPToesZFz9xmrdx7MClCeFIKIPowdCfTtkoiDX6/qVd41JCpaUP370q7GNICyJLpFSWFiouv6DDz7AiBEj0K1bt4DH2rlzJ8455xysWrUK3377LZYuXYqXX35ZdZ/Gxka4XC7JXyRRelALclMwsruvNg8/Xa7F7avd4wgqmRt72/N6t8XuEzXCdyPiU/5XfAT/Wr475OMEgqM8KT7oHhBRgpkuZfGEhGSnHSVH2f08jWviA0Mjkl5//XVMmzZN87YffvghevfujREjRuCxxx7DggULVPeZNWsWsrKyhL+CggLV7c3mjC7ZzOXPXDFQ4r7gBYm4to4jiLT4/At9xvk9JcvlrtkWA0TK3R9uwgtLdmH9/tMhH0sNN6XFJwjLk5EUmWwVKU47qhvYZT7O7dUWJ6ujy/pO6McwkbJnzx7s2bMHF154YVD75+fno6ysTHWbmTNnoqqqSvg7dMi/Nk44+evkAX7LslOdGFMkjbPhXRnimBRHCMncUhKlHYZbluXN7fHghKsBz3+7A4cr6rSfgMGRqoaQ9g8Er6cozTVBWJeGFrd0QZji3lIT7ejell2E9d8/7MOwp5ehorYpPI0hIoJhIuWTTz7BpEmT4HQ6A28MYNSoURKRsXr1anTt2lV1n6SkJGRmZkr+IklWqhPf/vFcyTJWgjfektLU4hMTzlY/a2W99geMd/ckOqT/bXKhM2fFXvz+nfWYs2Ivrp+7FqHQ0OQOvFEIUFp8grA2LW4Pmt2RicZPSbTj9nO7C99/uH+c30zAC15aGeZWEeHEMJGyePFinHfeeX7LKysr4Xb7v+j69++P22+/HWvXrsU777yDF198EdOnTzeqOWGjd/sMZIhyfLAsAnzitkbRaIQXLg3NHlTVN2s6F99NyEUKy7uzpawKAHDwtH5LijhOxm8EZTBcCIGz0TTVOli2llXhtv+sx54T1ZFuChGnNLT4T/VNVphxYxQX9MlHp+wUnFvUFg57AkpnXYLSWZegS5tUpMtcT6drm6I2TxQRGENESn19PdauXYvRo0f7rcvJycGWLVv8lr/wwgtISkrCuHHj8Pjjj+P555/HLbfcYkRzwo7YZ8oqtsVbVxpFD7s4BqPkiLYAYF48OGVCSC1dfjDUiqwnDc3mipRgYlLiKXzlild/wpKS47jlrV8i3RQiwjS2uPHz3nKJRTYc1DOsqXKhYDRv3nIWVt5/HlISvWKIT/gJAN3y/N0/Wgd6RPQR1C9NPiMlJSVFcfqw0uyV7OxsfPHFF8Gc3tIU5fuXLeetJj/v9dWnqazzuXn4B1GNFrcHy7afAOBvdTB6FLF46zHhs9k5V3iBRfXC2JN7eDN7WWV9eBtDWI6//q8E7689iJtGdsVTl0vj4TYerMCeEzWS1AdGwQ9Ukp0JaGj2CqQ0k0WKzWYT4vbkdMxK9lt2urYJuWmJjK2JaIfyDRvAdcO7CJ+L2qX7byB71mw2SJIQvbhkZ8BzbG513wCQZJcFgGYdmRdP1TRij2i6MosmicVH86GDQnD3UFAKQajy/tqDALwZpi/9x4847vIFtV/56s94YMFmvLx0l+Hn5d3UYhdP7/b+g7Fw0SHb31pd28ieAUREPyRSDKCHKPq8kGGKTJH5b5MddiFxEQD8uLs84DnEAbkVdU3oLjrP4Qr1UbZYxJz5t2UY/9JKHDylHKtitPtIDf5cNAWZILSz7YgLz36zw2/5378zPrdRfZO3/0hx2vHJ7aPw/G8G4cyuOYafRyupDMvzlDk/4avNRyLQGsJsSKQYgFOUEVE+/RgAhhRkS74nB1Ewq0kkNE7XNGHJvefi6rM6AwAWbTmqum+/xxbj5aW7JG6hTYcqFLcXC63jLnPzEPBtIkMKQeijJkzWA77vcdoTMLwwF781waWkB6Ug+7s+2BTmlhDhgESKAYhFinzmDeAfTBtMZLw4gLV723Q47AlIT9I23bvZzeHv3+3Gwl99eWhYcSxuD4cXl+zE2n2+2Jl5q0p1t1UPQp4UsqRI8NBsBSIA4fqJ8HGFVsllZJFmEGGCRIoBKAV48dhsNskDLnf/aEEcYX/NsILW4+o7xoYDPuvJX77wn3H17bZj+OfyPfjH8j262xcsvrT41POIee7bwHFKRLzDVilGz8jjxZBVHlHqK+ILEikGoFRKXIxYpChV9VSjvrXjGd2jjXAsvY9qRrLP8sJH6Ysprwl/imlfTEoQO8eYsUEcl/P6yr0RbAkRDShZUoye7eex2ECCguzjCxIpBuDUIFIcogeLFZMSqNAgPzoSW2H09hnipHMsMpOV3Ufr95/GiGeW4esA8S964RNZ6ukAbVSJjyDQNj2JubzF4OywVssKbZV2EOGBRIoBBHL3ANKYi2SHvyUl0OiHd/ckiyLbNx+uUtqcSRIjXobH4+Gw7Qj7eCerG3Hbuxtw3NWIO97fqOucgbCav5sgooX2rflC5AOcFo+xyd5CyQptBtRXxBckUgxAk7tHJGRYydvE8SJi9pfXYt6qUlTUeTMqii0px1z+xf+evWqgYhsaVTJVfr/rBOb+yA6S9XCcJHdKaXmt4nH0EpK7J8YI59RvIvrhBzbyAY4RVdDFWC1NgJpY2ndSPQcUEX2QSDEALe6eyjpf2maWu+eaf69h7nfeC9/jqa9KhPwHYpHCKmGek6qcdVEuUg6J6vqoWWVsNmn8y/c7Tyhuqxd+0GeVUVokkb9szC5JQEQ3fP4juSjRk9xRCx7BkmLoYYPGodKQD1oT3hGxA4kUA2BmmVWhpjH4l4/YCnPvhb381qu97BtlxQLFmWfVhBbHAdWinAz/NHD2j9tiQXmRRB5LQHkfCDX4QYdclJgVOGuVR9Sh0leZXRCVCD8kUgygXWYyvp4xBj8+ME7T9nwOjEmDOkiWf7HpcMB9xTlWrmEkVUpQ+R9tlM3oEdffqGtSTgzlN1IzsMCZLybFsENGLXJL17LtxyPUEiIa4JOs3ftxsWR5s8GBs9GUJoA1a5GIbujVYBD9OmZKUt3LGVOUJ3x2tsantMuUFsq69+NiP2uHHLGrKNGRgDaiolq3jOqqOvNFPjNHLAxqVaw78sRiFw9sr9pGPfhyMFi/AzQbo830RGzT2OxBfZPbT8yaFTgbDc8ouUhjDxIpYeLd348QPvPmytE92vhtt/FApepx5EG6p2p91ZQnD+mkapI9US3NgyIecbHKsfPILSlGpuMOJQdDrIWZkkgh9NDY4mbmNjJ+CrL3X6vEpKhBlpTYg0RKBOAtKef3yfdbJ/Yns3KnrNx1UvG4jgSbrpe9uDOrVXH3yH3cTQa6ezxB1O6JggFdUBh5X4nY56vNR/Hq9/5J//YZOPsOsF4yNzUCWaKJ6INESgRwtAaOsMynN85bi3//4O14WPFvRyqVKx7bdYoU8chdzZIif/CN9HkLo7RoGKaZjNFTR4nYogvDnfzhOv/ZLDM+NDbgmrNYMjcAeHRSP9X1H607iG4PLWKW/yCiCxIpEUBtCh0APPO1twQ7y7es9hpz2G26OhKxSKlrFSnn98nHy9cMxj+vO0NY98/vpLN5jPR5R9MojSAiCRchB6cV48Z+f04hc3laoncywEOfe8UJTUmOfkikRABxhtov7hjN3Mbt4Zjmf7VqwY6EBF0diXjkzs/uuX54F1xxRmdcNrijsG7xtmOS/YyypLg9HLYdcQGw1iiNIKyIwfGw2s9rQUuKEqxEmUR0QyIlAojn+Z/RJYe5TbPbwxQDbdKVk7V5Y1J83/91/Rn4z/8NV9yeZUlJTQr8kLcYFOD56fpDwmeypPhilQiCRRnD1ctyAY3r3dbQ8/oCZ63/+7TZ/GcjEtENiZQI4NQwJGlRsKQkyer+PDixj/DZnmCT5FFJcdoxtCtbBAFSi4ggUhLVixDybTOCXw9VCp+joQM0m/yM5MAbEYQIO6NwaWpS4GdYD1bNk8Kq4WO32YT8MURsQCIljPCzeW4c2TXgtm43x5ySKpcH6SLLh8NuQ6rI3GlPsKnGv7RILCled494/3SFzs6oWSjiziQaTMlmY3R+CyJ2EAevf3TbSFw8wJurSNxH3NeagdpoS4LVMs7yvHzNEL9lLR7OL2lloArzhLUhkRJG3rz5LBQ/dhGK2mVIln9820i/bVs8HmZBQPkDJ3YdORISJJYUe4KNme6eFyLNHpYlxbe/Uj4Uoywp4inQwczuibW+J1A6c+ps45caUZ2u4d1y0TE7BQBwuMLrAhpTlCdkkDY8Lb5F62tNHtwRgwuyJcua3B6/2YhkWYluSKSEkYQEG7JSnX7Lhxfm+i1TcvfIEQfSOhJsksCxBJuNaRLlhcgXGw9j/f7TcHs4QRBpcfcYZUkRWw7IkhJY/NEU5fiFHzCkJzmQkOA/i69TdopgNTVKpGwtq8I1b6zG0hJvRtv9p4zNv2IE8jCuFrf/4O5YlX+1eCJ6IJFiAWw2Gx6Y2FuyrL7Jja82H/Hb9vHL+isex263SaokswQKAJTXeLPUbjxYid+8vhonRZloxZaUm0ex3VIHT9cZkn766y2+WUNWG6WZQV1TC6a+8ws++eUQcz1vWbrngiLmespIG7/wFc95F6z8eenRNl1Y5jbI4nbr/F+wtvS0MLvvwKm6AHuEH3kl+BY352dJuffjX8PYIsJoSKRYBHlA7EtLdzGzSfbMl1ZcFpsykxwJEpGSn5Gk6dzHXd6RRoLNewwepVwEAHDlqz9rOrZW4kGkvL5yH5ZtP4EHPtvMXM+PlhMd7MfS6MJxRPQgiJRkr0iRpxqwJ/ispkZZUlgp962Gq6FZ8r3Zw/mlxt94sDKMLSKMhkSKRUiSvZj+W+xvRWFRWeer3ZPksCMhwYZnrxqIRyf1Q/e2XkEzrJvyDB8AqGg9RlqiQ9L5dW2TprhPyVEXdhxzaWqjFuJBpOw5Ua24bl3paeGzvD4TD1lS4pdtR6oA+Cwp8sfFYTdepKTJXL9KltVIIrek/LDrJBUZjDFIpFgEpdGzmA5Z/lNUWZbda4Z1kVhBLh7QQbL+2mEFku+nWt0/rERI4nPKCyI+/MXWgG3WShxoFJxwKY9MD1f4TOnNCrN8jC4cR0QPf1u0HYBvFl6zLO7CDEtKmixnEl/Ow0rUMcp58DE0RGxg7IR6Ing09CvyUQMA3DSqK5btOIHJogyxcnLSpMG63dtKLSRrS08BkMajCM0Stevnvack64xK6gbERwT+SRXzufjF0qBQR4ksKQRfyfwn2bPoEKUbMNaS4vvNOqIk2aC4MjwR/VhPGscp9RpMlKwpwdmpiVh459mq8SPZqdIstadrpX7cPSdqAASe2VMki4cpPlylur0a8um0weR2iFQtk2CR528QI36xJDnZWX9JpMQn4mcluTV2bUhBlmQbe0JC0IGztY0teHfNAZxwSWfByNMCBKo5Fgmeu2oQAODe8b2EZVRNPLYgkWIR5C+gIbL5/0DwadNzRCIlLz0JE1sTQfFU1ntFC5+xUswxUcd15dDOQZ2fhXyaYHTJjeBQc2m1y0wOuB0FzsYn4tl3/DMqt5bYE3yWDr2WlCf+uw2PfrkV1/x7jWS5XJM4FGKlIsnVwwqw9ckJmH5eD2GZ3AX0u9Hdwtwqwkis96sjAIAZ/CWuTKyHXJFIeejiPn4CyFXvtdDIZxjJ+d3obujTPkN1G62wEtVpxUrVWPWgFhzM34+hXbIVtyFLSnwiHijwyRrlPwWJJUWnSPl0w2EAQGm5NA+K/PdqRUsK4A0mFsf0tc2QWo7rFdynRHRAIsUiyGvsyEcDj07qhwn9pRYQrWSLYlJYplB+Gp9a8O7gzllISbTjgz/4Z8cNBnkug3hPpsrH5CQ6EjCqexvmNiRS4pPMZN/zm9E6BdkjzzxtQuCsfDBg9ZgU3h0tHwB9vJ6dl4iIDkikWIShXXLw/tQRwhRDsUjJz0jC788pDNqCkCGqwVPLiGvhhYuaSOFNvdkpvg5THqOih3isr6E2OcL3f2BXrIxNGWfjE7Eg4Z9R+W9BPrvnz58W46KXV6KqThp/pge54cRpwdk9Yvjrp5iU2MLav7o44+yeeeiW5y29Xt/kExMnqkNLqiQWN7VN7Ho8AFuk5KV7E8Jd2K8dAG8wHR+kxpoNpJV4jElRcvdwHIclrVk9+RwprNw28mmnRHwgfjb4jNP3jpdmJXYk2IQSGW6Ow4INh7HreA2+2qIt3xIL+c9VKYO1VeAtPSRSYguagmwxtpZ5E6TViiwpw7v51/YJFr7gYHqSQ7GAoJivZ5yDtaWnJcG2Z7W+QEOJKxG7e7rkpuIMlViMWEFJpHy+sQxLWnM78En9WEaTZrKkxCW8lTErxYlercVJ+USNPAk2m2DtFLsFQ3lhN7dIf2/BBu6HC3urpSce0hnEE2RJiQKmnKGcA0Urf53SH8O75eKm1qyRX955tt82bsbskfzMZFw2uKOkmjL/Ig1FpPCBwZnJDqz483kBg3ZjmW+3+WoY8aNBlvuLLCnxCa9N1QJXG1vcwnMpFibBJgCsrGvCzuPSDMlWnN0jxqHi7okHd3KsYu1fHYGxvdri2mFdQj7OzaO64ZNpo4QgvJ756fjrFGmxwuLDlZqOxQuKxhDSTy/YUAYAcDW0BG1GjqZ+53Rtk2T2xL6TNcJn8civ+FAlAHZ2zxaFTLREbMNbPNWSlNU2skXK019vx4lq/VWAX1iy02+Z1d09fPtYAeZkhIxeSKRYnOtHdDGtc5C/5I9qLGnOx64cCaEEeml5TeCNFLB2V8lm6FNLJd/Pf3Gl8JkvSwD4XEJJjJw1lCclPnm6NSW+GnXNbuG5rJAFy76ybLfucy7eesxvmdXdPWqWlFO11i+WSLAhkRLHHDwtLb3eLlNb1WSxZvp22zH85YstqKpvBsdx2HHMJdQXUePKM7yJ4dpn+tcjijcOier28Hkw5AUngfgILib82XCgIuA2ZxRkK7pMgym4V17jb7WxYu0eMfxgjuWG/nAtTUOOVqz9q4tDbj+3u+R7hYl1KI5U1ku+D1WY+ipHbNm5/d0N+GDtQcz+Zge+33kSE1/5EVfM+TngMR74bDMAoDBPudJyrCB27bBw1ftGvvxoVfzC6dha5JH86oSYia15ky7ok48BnbIUUwgY9bOxajI3HjVLitqsRsLa0OweiyHvT+QVjI1EnmtBayAsS1jsPVGDF1pjWuQBd3LENUL2BHiBxwK/eX216nrxfwMvAMWWlO5t03GkqiGqYnAI83n1hqGorG9Gbpo3w6qySNH+w3F7OEx7bwNzndUDZ/nZPftkmXMB4xLcieE4LmqzX0cTun915eXlKCwsxP79+4VlM2bMgM1mE/569uyp6VgrV65E3759kZeXh5deeklvU2KSjSLT7k8PnY+sVKfK1qEh77zEidrUYD2Y6/afxrYjLk377z7hEybBFBaMNk4HsIYNL/RNMedfBOP65APw5k3hb/eBU3V++xI+YsnSdKyqAYu3HoPHw2Ewo44X4M1ZxAsUgO0iBPQFja7ZdwpLW6fDy8kxsS8yAjVLjzxDb6g8sKAYvR75Bk8vKjH0uIQ/ukRKeXk5Jk2aJBEoALB+/XosWrQIFRUVqKiowKZNmwIe6+TJk5g8eTKuu+46rF69Gu+//z5WrFihq/GxyHqRSOHN/GYhHl2c26stHrq4j6nnY51XS66WWKdTdorwme9oJw3qgDdvPgsrHzgPP+4uBwC8vGxXRNoXDazYeQJDn1qK77azX7DRxtjnV2Daexvw2cbDGNzZW/F48mD1VASOBBuzOKWe17Pai/7MrtrcwZHCrhLYa+Rg6L/FR/DJ+sNodnP4fudJw45LsNElUq699lpcf/31kmUtLS3Ytm0bzj33XGRnZyM7OxsZGYGL0L3//vvo2LEjHn30URQVFeGxxx7DvHnz9LU+xjHblCieLPKf/xuOfAODWNVGteI1lOpdWgCNd/fYbDaM79cOHbJSlHYjRNz69i+oqGvG799ZH+mmGALvev1hd7kg6ru3VY/fstlsQsZiMXqsCHzgtpz0JIflXRtqAuuH3eWSatKhMOPDwINwwjh0iZS5c+dixowZkmVbtmyBx+PBkCFDkJKSgokTJ+LgwYMBj1VcXIxx48YJP/zhw4djwwa2L5SnsbERLpdL8kcEz8yL+8Bpt+Hu87W55/RQrzKjQCxgQjHDxoq8Ed8rp8X9/kR4+V/xEcFdo1ZFm4c5K0xPTIrCttaWJ17UUjWUltfi/Be+BwD8uPskPvnFmNk+NMgyH109YmFhod+ykpIS9O7dG++++y42b94Mh8OB2267LeCxXC6X5HiZmZk4ckS9zsSsWbOQlZUl/BUUFOhpflSQzMiPYRZ9O2Si5K8T8aeLeht+7JoGZTeO+LEORqNYfECnG3GJAKsnzCLCDy8ytPw0EhnTkPVknVUMMI2Cn2Wg2UfVra7lm+atwwOfbZZkeg4WqhNkPiG/EW+44QasX78eo0aNQlFREV599VUsXbo0oJXD4XAgKcmXlyM5ORl1deqBgTNnzkRVVZXwd+hQ7M19L8r3usrC9SI2a+RerRZrQoMPCeL3grhiNQuxoCHiA144aHG3sCwpeqyVrGytQFRoFN0C/6UlwcV4nSWKzaE6QeZj+BsqPz8fHo8HR48eVd0uNzcXJ0/6go6qq6uRmJiosgeQlJSEzMxMyV+s8eoNQ3HFGZ2w6O4xkW5KSKhbUnyd5ugebcLRHEsjNsc/MFE9eHngE0tQ3dCsug0RW9S1ugO1vIRZIkWPS6KukS2CrR6PAvgnpwxEvsbklXLEcTtKoo4wjpBFyv33348PPvhA+L569WokJCQEdMUMGzYMq1f78kds2rQJnTp1CrU5UU9BbipevmYI+nWMbgGmNmtHPLD7x3VnhKE11oZ/h7x+45loH2BGV1OLBz/tORWGVkU/n6w/hCte/cmwgMlwIk5Bv2izd8Cnzd3j36UHyhEiznskjo+KNs+jFquw+F4oTdkOxKo95cJnKvppPiGLlMGDB+ORRx7Bd999hyVLlmDatGm4+eabkZqaCsAbe9Lc7D/ymzx5Mn766ScsW7YMzc3NeO655zBhwoRQm0NYhGo1S0prPzG4cxby0oMbzcQSemIOABq9aeWBBZux6WAlnl28I9JNkbB46zHsDZDEMJkRW6IlcJZlNeGnsCshzrVytMqXhTot0YE/jPHGDT46qV/Ac0ca1j2TIy6cGEzMa5WsLhK5e8wn5IyzN954I7Zt24arrroKdrsdN954I5555hlh/aBBg/DKK6/g8ssvl+yXl5eHl19+GZdccgnS09ORnZ2N+fPnh9ocwiKoWlL4D1FgQg4HemZvACRS9FJZZ15pCb38tKdcyOi6f/alitux4ki0uFzKa0KzGj3ztU/QeTgOf7mkL247twfaZlh/MOHUYBl57fu9wudgkv+5ZK7WZjdHmWdNJiiRIv/PnTVrFmbNmsXcVp74Tcy0adMwYcIE7NixA2PGjEF6enowzSEsRJIjAY0tHtSoxE3wvx96rL0IlhSNdk0SKfpYYaGEW1vKqjRtxxrlaylCzMqTEvhc7Je1h/MKo2gQKID//RnVvQ2qG5uxtYw9iSOY+H25SAG8QiXRQb2ZWUQ8KUNhYSEuvvhiEihRTOmsS4TPAzt5s2Oqunta/w118BHNadDFsQP8C0nraKxJx5RSwpy6LcGitS0s4ZCgwR+oVL9H/Vzs5Wq5jqyI3BL5j+vOwO3n9jD0HFX1LJFCgwYzibhIIaIfm82GRTPOwSe3j0L/1oBftQeX73+D1SixYFnNTfXFAXgCWJb+db00uJiC9fRjlXT5z3+7M/BGYIsULflOgkopEMViX4xc5LfNSMKxqgaFrYHtR/UnA21gCDfKlWIuJFKIoOjRmqI7L937su3fMQvDC3OFSqTNqiNG7XkfYhVxZk8uQExKvw7SmV4UrKefaEqXz3EcmhmCRM06yRMooRkLCxmaQkJ86R1aZ8mNUklxcNylP36HJUjIkmIuIQfOEvHJ278bjjkr9uC2sd0lyx2tjmE1s3aolpRYoEFUr8cjzO5h3xG5mCNLSmygFHCpZNjQkh8nmIzFRlcIjhTi56drG+/s0j7t1evI6Q16bWQ8ezRoMBeypBBB0aVNKp79zSD0aCuNJeI7SVV3T+u/4TCkLPy1DE99VWJoFVQjqG5sEWZi+Cwp7G3li2nkFhss2sJOeKlUP6e2SYMlRRQ9Oqq7z4qgFr9lsUcjaMSB53w/FEi0ldfom/nFGnyRu8dcSKQQhsL7bN/+aX/AbW1hsKXc89GvmLeqFEstEpMg5sY31wIQxaQoWlKk32PknRL3KM30uecjdpVdLQYPu+hN/U9RLJOaEInmAHQx4ueH71sCWUlq1cp3MBCLlDat+WVYrjnCOEikEIayZt/pgNt81FqBdN3+wNsahRWzju44Vg1ALFLY28nFXIy8U+IepRH411vYhe+0WB7FMSnimT5q7lcld8+Lvx0c+IQWQmw0Ed+rmRcrl5pQslopwd+rC/rkC0HKRyrr8dmGw8ygWiJ0SKQQhtJGlL1S6aH9YVf481ZYcbTYPtMb3BcocFa+OFZiCIwkWmKw+QyuADu+Qf13Gvgixe4Nu+imaIkRk3PVmZ0Dns9KiJ8f8efbx/ZAr3bsFBd63cC8pzUhwQZna26UW+f/gj99Wox/Ld+js8WEFkikEIZSkJsifH55aXBVRrWip3uxUq4MnsEF3pwyfMu0xjxa70oiTzBJzCJBkih1uzwA+lRNI/aV1yru2zM/cC4pcc0fsWBp8SjHTcSK6BULE7loTXGyU+br7RZ4y4vdZsOh0/WSdcss6FKOBWh2D2Eo4k74jR/2YeYlfQ0/RzCxLBbUKMIItrT1xaQ5JsWC1xJptJYUiDTiCuDiWSFVdc0482/LFPe7+/yeuHlU14DHF98HsesnGEtKtCERKbJ1FXXsmVF6By+85YUVkBstv8FoIzqGH0TUEGxlUbNZtac85LomRuPh5LEy7A5TLl44sqX4EczU20ggfifubI1JAoBtR9XT5f/pot6aErWJhYldo0hhWVIuGdg+4LmshvgnIBcMB0/XMffRa0Xi7yMr+2+sWKSshjXfKETUcvHADpLvLRaZLrt8xwlc8OLKSDdDwo+7T+L/5v8ifFfq4/y6Q+oL/ZDfIyvGIAHS/+MdIpGiJO7P6JKtK4BVPLvHZrMJL251keK/7JyebTWf0yqIdYlWo4ZeYeER3D2hH4vQBokUwlCGFGRLvr+75kBkGsKAVXcj3HTK9sXsNLZ4JNNQlbo4moIcGPk9suq0UCUrmJKV5PPpo3UFsDplb09Hq2hp8XCoaWzBJX//EWf9bRmKD1WqtiklMfpeDVLrifQ+XDm0E3Mfve4ewZLCUEFWjHuLBaLvl0hEFT/vPeW3rDDPm1L/nf8bHu7mRJzXbhyquE7ZkiKfgkydoRy5u6exxXrTQTmOwxsr9zHXKcVZ6S0d0SU3VfKdvy9uD4cF6w+h5KgL5TWNmDLnJ2EbVkxtsoMdaGplbJLZPdJ1vdpJM8/y90mvruC3Z7t79B2L0AYFzhKmUlHrn9GRN4umJ4X/5+fxcJqqyZqFWnCdkvjwn4JsZItiA/l9tWIW0FV7yhXX6c3XocTdFxShvKYJkwZ73a5ikaIkeFi/u+TE6BMpajEp3VsHRjz8fQne3WPD70Z3w/yf9/utI4yFRAphKqyXsq9WTbhb4zV7J0ZQpKgNjLV2cdQX+iN/AbNykESakiPKVXe11OXRQnqSAy9e7Yth4V/GLR4ObdITmfuwRG9SlEzpFqM2BfnCfu0wpCAbvx6qRPvMZKHv0Z8nxRc4K68LRCLFHKLvl0hYHrEGuLBfO7/1vHk55Cl7QfQJkfYbq81CUVojX/7f4jLD2hMriN+pHMdZ0pKiNrvsf8VHTDmnQ2RJyUpxMrdhvVyjZbaUmBxRIkl512Kz2fDFHaPx/tQR+PqeMULfo9eC5RamIMPvfqqkoiFCgEQKYTjiUS2rAxRMpkF2hKFom+3HlEez4SA7JVExKVeSQsIpuUrRWxQtHhAL3o0HKyxpSVFyM5ZV1uOT9YdNPafbwyla4Fi63RGFlpQbRnQRPrNcWzabDWf3zENuWqLP3aPzZyJ294wQFXAUryOMJfp+iYTlEXcPrBFtoFo1ZnLlqz+H/6QibDbgs2mjmeuUxEu0ZFONJNLfHIefVOI/IoUzQfr/mJ3qHYkfraxnbW4IYkuK8kvUf7l8llA0kCwS+YGstPx6rcKiprEFS7YdQ22jNyDbnpCA3DSp+4xEijlQTAphOOKHtYmRJ8VtlLsnSklRCEpUCiROYyxvdns0JfeKF8Qj5+93nVCcRRNJ5IaUdhne2k1mWi18MSkeP0vKR+sO4qe9p9DY7P+MZqew41eihUA9C68Xtbp7Zny4Cct3nBC+JzLy2rRYdNp7tEMihTAcsfmYN7tX1jXh4r//iJ756XC32lij0e8dKk0tHt2jVJYYWfjrEfwmygrAmUmS03ePrChQAH+3Ci/gHSY+B2qWlIc+36K4X5c2qYrrrMxt53bH/J/2457xRarb8cUXtQbOigUKwBYp0XrPrA4NxQjDueO8HsJn3t3zyJdbcbSqAT/uLhfqaERKo4Q7qZtYlDS5PbpzX7BgTe2OZwINiLtZ4AUiFwn8s5Fq4nTfBIlI0bZP3w6ZprXHbP5ySV9s++sE9GirXozRJrh7gjsPK0Nws0Wya8caJFIIw/nTRb1xQZ98AF5Lys97y/HV5qN+2xnxsg6GcL/gxddZkGPMy7KmscWQ48QKgWZtWcG1KHct8C81M50E2mJSpGw/Gtng8lDR4gYV548JBlacmBVnlMUCJFIIw7En2DCsMBeAN/Pn9XPXsrcz4cWxeOsxPPHfbWhxexSTo4X9hdXajO//fJ5gJr7mrIKQDinPLBrvBHrZWCGoUd4E3t0jX26k+8cuSosvLWYZ3/B9T7DZm1nuHquWYoh2SKQQpsCPNNSmgoYqFlg1R6a9twHzf96PzzeWoVKhPHuTO7wp0/l2iuMm7r6gJ2Ze3Ac92qYp7aZKXkaSIW2LFQK9bKzw+pALqeYWXqT4lrfLTMJVQ42LNeIH/G4Ph0e+3GrYcaMd3hK5bv/poPZnixSypJgBiRTCFPiHuFbFLRGsRtGy29GqBry+ci9zXbhzaAj1PkQX3DknFbeP7YGMZHaCrUC4KXOUhECzNKxgSZFXBOdH3uKWcZxv5okRiC0phI+SVpfW2z/tD2p/fhA2vNViDJBIMQsSKYQp8CKlukFZpJg5u8fNcahvZltMwm2WVcsLc9e4ngCAK85gV2lVgvpDKYHuhwU0Chpap/r2bi1219TqkhQLqCuHdkaHrBTm/sHAP2JUlNJY+P7twz+MxAd/GAFAOgWZ4zj8fdlurNh5grk/oR2agkyYAj/S2HCgQnEbM2NDPB5OMQnal5vKMKQg27Rzy+EYlhSe8f3aYc3MC5Cv030T6fT+VkPJUpKR5EB1Y4slRApfmXnCgPbYebwagFcwi9t274VFqG10Y+PBClxpgNsnIYhZLJcO7BDyeWMJlsDjRYo9wYZ2md58N2JLytKS43h52S4AwP7Zl4ahlbELWVIIU3BoyAVi5hTkFg/H9BsDkFQuNRtxB6d0ue2zknVXZiaRIkVJpFS3uhutYEng3YyZyb6xYbPbI7Q9PyMJSQ47ctMSMf/W4Zg8uGPI5wzGkjK2d9uQzxtLsJ41cd/CZxIWW2gPV5iXRTjeIJFCmIJDg2Nd74tZDzuOuRRFSjgR929GWo70FkaLdZREGz9Txgp3q6HV/SjOLNzU4ssEa4Zh0dYqjas1TFkfU5SHthlJuHhAe+MbYjH4FAlaYMXziKtEOx2+rL48Vvi9xQrk7iFMQcs0SjPdPadrmxTzJeQplKw3A/EI31CRQoGzEpRECv+CsULgLG9JSUm0w55gg9vDodktEimaQsL1wf/kHliwWVhWlJ+O3Sdq/Ladc8NQpDrtUVlcUC93jOuB73acQFcNSf5YAbFO0QDIIbKkcBwHm81mCctdrBD7v0YiIoTD3aPWD7S4ObSRFQA7u6e3aum5vcJnzha/HG0GPm0UOCslUCKtSL4zFv5ahoe/2ILaJq8lJclhF7IQN7Z4hCnqZhgWWbr4YoWYE0eCLS4ECuCb9aTFbXrgVJ3fsmNVDcLnDJH7TintARE88fGLJMKOqe4eDRYJD+eLSemel4YdT03EuN5eE6/Weh1GwBnk7jlPFicQzmuwOm4PF3CKbSRv1z0f/Yr31x5E8aFKAECyM0Gw8nljUrzbmZGBmVX0TsnKaYWsvOGCT+YmFyl8cLOY99ce9Fsmvlfi6ssfrDsIjuME1x4ROiRSCFNQsqQU5PqmV5rZKbaIapUU5KYi2WkXVYSNjEgJ5WpfuWYIHrm0rzArifJe+NCWjtw69yvJYRdqvzS7OeFFacaUfNbvRMl6EE8FP1lp8d9bcwC9H1mMxVuPSbata/KP5+nfkV3f6Plvd2L6exvxwpJdwjIKcg8NEimEKSiN1oZ2yRE+m9knuj2cYG3gOyRHiPU6gsGomJTs1ERMHdMd7VunO36+8TCOVrFnELz2/V7cNG8tc1QYi5TXKKd75+9XpN4TrN9akjMB5TXe+lEf/XJQ2MaMasisy1ZKOmZGmQqrwvcJJ0SlAviMvNPe2yDZlvXbUYtlWbxNKnIoyVtokEghTIHV39lsQIrINGqmJaW0vFYQCHzfnxBhkWLE5dpbLVTrD1Tg0n+sYm7z7OId+HF3ORb+eiT0E0YB/y1mX+f4vu3w7G8GAYjcFOQaRjLDZIfvGXj7p/1CJlozLBms62a9YG02c2fbWQ3xvV4iExVa0OOaa7K4SKmsa8LTi0osW1iSRAphCu1FWTP/MKYQb986DD89eL7k4TbbB87PYODPGQlLivhMRlyveLR7OkA15/qm+LCk9MxPZy5/85az0Cnb+zuMlCXF1eAfSCmu4STexgyRwprVNHlwJ9xzQZFkWTxZUQCp1eq2dzeobAmcW5QX0rlOuKxd2PHhL7di7o+luPjvP0a6KUxIpBCmwL8cAG9cxrje+eiYnYJtR6qE5WYP3HgxIlhSbP4mXrPhRIMoQywpcTTa1YqakYS/55GypFTV+4sUeSbkulYxaY4lxX9ZSqId917YC+P7thOWxZMVBdA2+5BHS76lPu0zFNf95fMtms8VCX4pDa7IYrggkUKYjjjx2ObDPpFi9gtXHpDI/7ulrAoHTtWaem4eo/OkkEjxhzenj+ye67eOv+eRCl10MUSKw25D9zxf9es6YWqy8d2xmgXpqcv7C5+1BR/HDuJBVK926Xh28Q7FbbXo20GdsxTX/XLA2iLA6hZX3U9FeXk5CgsLsX//fmHZwoUL0b17dzgcDgwZMgTbt2/XdKzJkyfDZrMJf+PHj9fbHCIKEE+XzRXlLgl1yiWr88hJ9VUVfnfNAcl5xAFsCzYcDuncWpG6e0I/nh6zfLwklOJfsIkOO87okg0A6NHWKwL4uxWpW9HACF5OsNlwus7nquPdPWlJxufWVPsN8EHFAPC70d0MP7eVsdlsuGFEFwDAhf3a4bXv2RXTAX+X2YMT+/htozYAsborrc7i06V1iZTy8nJMmjRJIlD27t2LW2+9FbNnz0ZZWRl69eqFqVOnajre+vXrsWXLFlRUVKCiogILFy7U1XgiOhBPg7z3wl4hH0/tkWeNHPkOhK9CCwCjurcJuR1akAbOht5ZBTpEvAgTMbz4TLQnYNaVA3HzqK74+PZRAHz/9zWNLXhrVSlqNKSHN7Zt/v8fNkiTfj23eCeA8Ll7hHaIfkyVderxTbEILwrl/0c3juwi+c73KWN7tcX+2Zdi+nk9/I6l5hKyuvXT6lOkdYmUa6+9Ftdff71k2fbt2zF79mxcffXVaNeuHaZPn45NmzYFPFZZWRk4jsOAAQOQnZ2N7OxspKWlBdyPiB74yr4X9vP5vpNMzmjJSnLG9xH1ohFDuB5LXqQYNZj66JdDku/y4FmrdzhmwFtSkhwJ6NM+E3+dMgB56d7fnvi+//WrEnwWJgsaD2v6qZJY/X7nScPPr7UcwOk4zJSa3Coslm0/LlleJ3N/cLJZgizUXHX8b5EIDl1vjLlz52LGjBmSZZMmTcJtt90mfN+5cyeKiorku/qxbt06uN1udO7cGWlpabj22mtRUVGhpzmExVl671h8eefZGCtKQ5+caFfZI3RYyavsgiXF1/mE62XOvyPMmsl0pFKaK0U8KnzifyVobHHHfLwBf31ORjCk/LZ/t+NEOJoksHy7//nCObCWF6K8ZVRXyfebRnq/33NBz7C1ySrwfdG+k9L4tM83lkm+a3mGxTEuci7oq72YIeGPLpFSWFiour6pqQkvvvgipk2bFvBYO3bswODBg7Fo0SKsWbMGpaWlmDlzpuo+jY2NcLlckj/CumSlOjGkIFsycryoXzsM6JSJm2WdpVGwxAd//ivO6CQsC1fBOV8HZ87xT8oSmTXLCg/2fmQxej3yDR5YUGxOAywAHzjLMrnLrRbJYa6M/fmmMr9lZqS/V0L8An5/6gg8fGk/yfq/TumPzU9chDO7+gcdxzrinE1ikmVTxLVYQ68b0UVxXckRek+FgqFP7OOPP460tDRNMSkzZ87E0qVLMXjwYAwcOBDPP/88FixYoLrPrFmzkJWVJfwVFBQY1XQiTCQ77fjq7jH465QBphxfPnIEfAKha5s0dMn1JrIKl0ipaPX1s2ITjMAtO26zgtXkk/XhdXOEE1/grH93JheHVohhVBKs8vpMRnN2zzy/e2Sz2ZCZ7FTYI7ZJVhAp4tg1AJpqKyU57Pj5ofPZ65zRMYlWLs6sgmGtWr58OebMmYMPPvgATqf+H31+fj5OnTqFxkblHBYzZ85EVVWV8Hfo0CHFbYn4g+M4piVFHLiW0zq7yBMmD8jsb5SnNhqBXJSpiaFYDarlLSlORryTTRZmnegw192oBaWX3QMT/GeNEOahZEkBpM+K1irVHbNTsOOpiX7LNx+qYmxtPVITjZ9dZgSGiJTS0lJcd911mDNnDvr16xd4BwDXXHMNVq3ypfVevXo12rVrh6Qk5SCjpKQkZGZmSv4IgkcpzkT84ubDFlgWFzPYfbza0OPdfm53yXd5oLBanZBYLUrYrMOSkp4UeZGi9LKz6kg2VlGypADS4FmPjriyZKcdVw3tLFlW3dgilD7g+W77cWw6aK0YTDXRFklCfirq6+sxadIkTJkyBVdccQVqampQU1MjKFGXy4XmZv/I8YEDB+Lee+/FqlWr8OWXX2LmzJmYPn16qM0h4gj5K1fpJfzZRp+rQ0juFSaRYnSgsDwzqPya1S4rVgNoeUsKc+aY7L1idikGLShZUhwJJFLCSYrKs1ktqrnE6Zyhx9quQfTsbThwGr9/Zz2uePXnsE+JVyPNAgKeRchPxZIlS1BSUoK5c+ciIyND+DtwwJtIa9CgQVi0aJHffg8++CAGDRqEiRMnYvr06bjjjjvw8MMPh9ocIg5Q6iy0xJnwL/n7PikWkmjVNLaYVqk02WD3glxoyK9ZzUIUsyJFxZIid/dYoSKt0rsuOZFESjhRsxzUNPoG1pyGmBQxP+zyn0oufvZmfe1zAR86XafpmGYhtj7HlLuH4zh069YNADBlyhRwHOf3x6/fv38/Lr/8cr9jOJ1OzJs3DzU1NTh69Cgee+wxOBzWvElEdKDFncEbIuqa3Hjsy61wNTRjwOPfYvxLK01pk9poLRhuPbubZKqt3MWlJtSsXo01WNRiUuSulRaTApj1oPR/ZFVze6wir9/z1d3noEOWNwuv2N3jqwGmTaSIa4Px8XBikSLONsyq7RROapt8lpxUk9NDBAtJdyJmkM90YSHuaErLa7HhgNcvfOCUOSOanm3ZFXqDpXNOKrY+OQHjWmeC+Lt7yJIiRj76bbZAXE6GwmwatRiJYBnaWiaA8Ecez9WjbTqOVjUAAHYfrxGW//WrEgBA8aFK3efgi0mKq2GLf5FvrSrVfUwjaRTNZNJSSDESWLNVBBEEWiwp4pk+HKQl282gbwdvdVQ++64RJDnswnXIO1q1WyBP/BYrNOuwpPyv+AhO1YSvCvaYojwAQGGeNJv2zIv9Z/Kw2h8q94z3lqHITo3PacZqyK2Q4r7hT5/65xU6GIRrhs9yfdHLP+CnPeUApMJ5Sclx5n7hQnwPrDr5j0QKETNoiUkRdxAejpN0TGbGKwwvNDZZFm8RksegqGXSfXThVkPbYBX4a2YJTnlMCgDc8OZa09skR5x1GQBuH9sDW564CP06mDtDcWyvtvhs+iis+NN5pp4nGpE/O0ZZEh6/jD3DlX/+rFTKR3wPVu46GfEYGRYUBELEDEqWFHG1V7EbmuOkMyp2Ha9G/47KJdeDwazBCe9P97ekKJ9xl8iEHUsIU0RZIoXx3jnuajC5RT74/48hBdn4bPoodBSlT89IdoYluVw8ZpPVQpIoqP3RSWxhseHAad3HvWVUN/Rql4EBHbMw+K9LhOV8eQ5xgOrE/u11H99I5P3HmOdWYP/sSyPUGjZkSSFiBqWYlPf/MEL4LJ1aCLyzer/w/f/m/2J4m/TODNAKb0nRMwU5Iyk2xyRqgY2su94hS7nOitEIbUuw4cyuuX7n3nsyNoVjNDC0SzauHVaA359TiN+fwy75ctVrq3UfNyHBhrN75iFL5mLjrbYDO/kGQpGeER/IRV7d0OyX4yXckEghohZ5kGgLI43sp9NGoYcoeHX9AV8CJQ/HSUwdx13Gxyrwhze6L+I7PLl7R83dc/Ww2CwjwVsrWGZ0lnApOepCXVN48lPwP0m7wttInoKdCB82mw2zrxoksaLc2xrDc5GocrtR8IKAE3U6ka5azjr/fZ/8KvStZz61DD0f/gZlEYxnI5FCRB1KL3yWq2NYN6mpW+x3bmzx4OKB5ppb9SaC0govUpZtPy5JCKXm7rFbyRluIPw1s65P6b6/smw3/r5sN+775Fc/k7eRuIW2mXYKwkDaZ3kD3I0SD4mi//g9J7xWM/GhI523h9VffL6xDL/sr8An6w8J0/tTIzg9nh4dImbQMrvnicv6C59Ly2ux8UCl5uNX1jUFnanWcEtK69t3zb7TmPbuBmG52i2I1SnIvLWC5VJTym3x7x/24eVlu/D5xjKs3O2ffMso+JedXSGb7O9GdwMAFOUbO1WdCA4+TqWhxR1gS23cONK/2ru4DxHnY4kESnmDDlfU4YEFm4XvRud70gOJFCJmkD9wrJkTKbKsnm/9pC1PwdKS4xjy16V48n8lutpkVkyK2GqwqnVqI8AeGfHJ32I1mZtgrQhwj0d2ZweQ1jSY5/rxBLCkPHRxHzz3m0GSuCkicuSley0pJ6uNcf2KE8YNKcgGILXSGHWeYFGyvP7liy2S70kRzKFCIoWIGeQmWtbgNdiRy6yvtwMA5v+8X9d+vP/ZaEsKayYL4B+tDwAzzi8C4CvEF61wHIdRs75Dt4cWgeM4eDwcNhyoQG2ru4t1S8S6pbtCYj0zowICZStNdtpx9VkFyM9IZq4nwkv71oyzfFI3MfNvHab7eOLBhKs1u+wn6321xPaV16KhOXLWFCW3ljxWyuhBlh5iM9yfiEvkeQ9YL4ZgX9TBWiGEJhn8jCsloeP7nCRHAgrz0nDNsAKhI4q0/ztUlpYcF14e3247hpM1TXj0S1/uF1ZHKraudM5hz+oxs9ikz90Tm/FAsQYvUqobWvyK//UIInu0U/T/3qjQ9+w9WWN46gOtaKkGH+kZSCRSiJhBPiq48oxOftsoWSAAIF1hiu7p2iYcrgguut2nUcyZgux3vtZOp1ubNCz+47kAgP+0TrNutkDdmlC4TRR7c6yqAR/9ckiyniUEHPYEPDqpH2oaWnBOzzw8h52mt1OMR6MrirAG6UkOpDjtqG92Y/1+aY6UYDICi2ORyirrmXFhkcz0qiVAONKZaMndQ8QMfExKh6xkfDB1BG4a1c1vG7UiYZcN7sBcPvSppUG3yReTEvQhmCiNzN2M2UR859poUDCgFejVPgM7jlVLlinpz9+fU4h7xhdJ8lOIMbMTFudJIaIDPpX9796W5k1y2vX/H+ZlJEq+7z5R7Rcr98CCzdhyuEr3sdX4dtsxvPDtzoBWQv732b1tmqUy4YohkUJELfLHj3/gslKcGN0zj/kiVxMpTS3+D7RR01ONfv5Z7h6O44SAN/F18psu237C4FZEDlbivUBCwGaz4ZZR/rMtzDRy8D8fcvdEP44gLCm/PbMAuWk+obLreLVfsGrJURcu+9eqkNsn5vZ3N+BfK/bgzR/VJwbw/ZsjwaZY+DLSkEghog6lIC63Sr4MHrWCgqy4EyU/slY4k8IyWS/kg6frcOi01y11utZXDp7PzxBLsJKgqQlQHtaLRpwe3Wj4OCCzC1kS5pMYhEhJdCTguasGCd/v/bjYzwJoJk+3Bvwr0SIK7E5TmGb85Z1nG94uPZBIIWIGtyfwC6FDtvIsiqZWd0hTiwfLSo6jqr5ZMP0Gi2nuHsYB95XXCp/FQX9i7WVmkGik0RL34QjCZB8KfCyTGRWOCXPITGbHpgXj7gGAQQVsN6NZutXV0Kx5W/HA7iijplVWilOYOh0p6MkhYgY+JkXN7H9Ozzzcd2Ev/PbMzn7r+KC211fuxdT/rMf/zf8F1ToeeDWMDpxlWYsaRdYFpSy0MaxRNAlBJ2NeOqucghH8r/iI8Lmq3pjfEWE+VzH6hqem9A/K3QMA+RnJOKNLtt9ysxId//fXI4E34tsgcvew+oYnJrMLL4YTEilEzOAWPXBK2Gw2zLigCJczZv7wrp0vN5UBADYcqMDlc36SbKN3NGV2WnwxJUfYwXdi64la2vxoRz5llAXLohHMLeE4LqDwuPvDTcLnnNRElS0JK8Ga5RdqJek+7f0TS8o5eKoupHPw6PmttQQI7J40qKMhbQoFEilE1LGhtUjgvpO1kuVaYlJ4slJ8QWL/uO4MAL6XnNhtUlEnfRGd3TNPczsXbz2GF5bsAhAekfKP5XuY214/whcsGuF6ZqZyQkP2Tpa7Jxjh9udPN2Pwk0vw66FKTdv3aZ+h+xxEZJg6prvfMoWqBpppm5EUcJub31ob2kla0dPX8JYUu82GK4f6D9ys4KaMfAsIIkieXbxD8l1PRdn+HTNx57geePqKAULA2ObDVQGn6ep5aKe958vrYfTDLhcpau3uKIrDMSuQ1wpM6B+4ci3LEhaMJeWzjd6soa+uYAtDeeI8moIcPWSlODGmSDoY0RKUrQYrzqUwL03yff+pOkOC3MW5T7q3TVPZUjqwm33lIHxxx+iQz280JFKImOHBz7wFsdbsOx1gS6/b5/4JfXDDiK6SF0igTiJYb4nhIkXWaS7ZdlxxW3EHG43enk0HK3DFqz+pbtOnfYam1PKs/4c/fvwr5v6wL6i2KRW1nKMgXojoQF6rJlSNmSETKX3aZ2B4N38X0viXVoZ2IkhFSqBkbeKMyImOBJzRJSfiGWblkEghopYrZHElQZdX17AbyxSqRr2sRpDRIkU+MlebKi3udKItJoXjOFzx6s/YdLBSdbvZommeaigFPwaaqilvE09lXRNzm6OV/jMliOjBf1p6aG/u8X19Vr4rh3bCN/eMUbRqLt56NKRziftBeT+ktK3YMvvKNUMAAPdP6B1SO4yC0uITUcfkwR3x3+Ij6N8xcDCaFsQPdUOzB8nOBD/X0Vldc/H5xjJoLUf3xH+3Sb4nGjz1VX64g6eVg+7ElpR1padxXu98Q9tiJne8v1F1/Xd/Govc1ETkpGkLFnSGOCSuqm+WBOhuVBBPsZTdNx5JNNiS0iY9CRseGQ+bzSYkd7tpZDdJsUGeae9txP7ZlwZ9LnF/VhsgmJwlUqYM6YRzeuZJktBFErKkEFEHH/wofhhbQiieJ7YunHA1CFOZxeg1hHy8XlpXxnB3j+x4K3edVNxWbEnRElxqJb7Zesxv2ewrBwqfs1OcmgUKEHrg8Bl/XYKzZy+XLGONVvfKgrqJ6EKeuC3UmBTAK1TEL/6BnbOw7i8X4KeHzvfbNpR8RuKigbVNbtVZb25R4Ky8rZGsfCyGRAoRdfAPlPhhnPqf9UEfr7uouunRqgbFOAMg+JiOYHMsKCHvVIpFs0y6tUnFGzedKXwXd7AsARZtpIv8+w6d0y5CyVfCcRxT5FTWS10+VXXN2FJmbC0WIrz4W1LMeWHnZyajU3YKpp/XQ7K8OIRaPnKr6g1vKs8a0jMjMlKQSCGiDsGSInrhfr/TZ0l4YKI+X2rP/HQh0l4pxoCVjO3V7/fg4S+2aBr1BJutUgk1zbP4j+diQv/2wnfxmc1KXGYGSvc1WRQvoDeDbINKBuHvd6rXNlKKeaqUTVPfWx57ZQjiDblIMduo8ODEPpKBxenawBbP07VNzN/ka9/vlXwvVpkm72G4e6wGiRQi6khgWFLEsLKKBuK83m0BeIt9aaHF7cFzi3fi/bUHNe0j7/RCxa5wjWd1zUGy0y7b1tcBtc9kz4BZsOEw7v34V2Yp+Ujxw+5y5vIUUY0RvZ1rklP5/2GVwvl4mhWsUJ9tkMYVpIral5eeiHm3nKWjhYQVkLtnw+H5mNC/Pdq0uoMaA6RT2HHMhaFPLcXv3l4nWa4nJT4QOJmbFSCRQkQdfEZZpZFtMA8cf8z1rYnilODPOGeFb7SixYVifJ4U9vJOOSl+y2w2GwZ19tYPkdcicns41DW14M+fFuOLTWX48tcyQ9sZCu+vOcBcXpCTKnzWe19vGdVNcV2gOk1KAu7NVaUSgcMbq3LTErH+kQtxQd/A+VsIayEfVBg9yFCid2vSP1axUzEf/+KNeftRJqxfl1lRAqElS3ekodk9RNSREEikBPG8bT/qrUwqN90DwBOX9fObgfjysl3CZy2jeXlyr1BR8pErxWhsbvVx/+mTYkwZ4ptOffUbqyVZU3ccDV+F1kC0SWdn6cxKceLHB8bBYbfptqSkMVKe8wQ6ltqL44UlO3FOawIwPhA7mKq5hDWQz8bTkoPHCHjRrWS142Gl7p/9zQ68vtJfpNgTbOA4jhkIy/9WtRTnjBT0FBFRBytwVkwwQW6r9rBN/UO7ZON3ZxcK31lxEqzoeXmiJqNrtyiJkUSH+rXLg4I3HKiQiL23firF5sOVIbfPCHooZMt0OmwoyE1Fhyx/q5Ee5FlAAwXhqglNcfIvvuO38OCUCIDYcnLtsIKwnZcXKYFmK8pdugD8BMqHfxgJwDuYU5oMwJ+G3D0EYSB2RuCsGLXRsl74Kbtqj/BN8/yj5+U6SZ5mO1SUBulaZrvUNXlFlVJg6uR/qWd3DRdK8chGWSjkCfDqm9VzSpTXKAczJkpEivdfK3f8hDpiN2I4EyDyg4yHPt+Cfy3frbhdikikKD3H4rT7SpfgbvVNWtndQyKFiDoCWVLaBJGE6N7xvZjLZ17cV/KdP+ONI7sIy5rdHI5W1Uu3kzXNaJ+2krVIKQGTeDR4slV4qWWptQKs/1+bzbiZCJmiIpMAUNuoHpPywILNiusSGS81s6atEuazVlRag5VwzSzE4ogvTspCHJxd3+zGPR9tkqx//cYzJVP1lYQWWVIIwgTsspgUeUBjMC+xs3u2kXy/d3wvrHv4Alw6qAMA+Plz5QGbMz7chGNVvlTo8k7BP812aChNvVUqKFbUzleFlx/p16mkzOY4LqSEUkbA6lgT7QmGJZnKSHJg4Z1n4/9a3Xn/LT6iuv2OY8rxOt/tOIEfd3unwXPk7ol6slOdgTcyAa2B4GJ3T7/HvsXCX6W/3TO75kisv4qWFIpJIQjj4UUIb8EQZ/xMdCTg7J76XStyEXHzqK7MYDn+YZcLo1/2V+D6N9cI38Uv2D+MKTQ8D4HSKL2HKDGdGLHg4NumljJ78r9+QuHMr0NKfhYqrI7VyGDUNumJGFyQjZHdvfFDoZq8b5rnnQ4quHss3PET6kwb2yPwRiag1boZ6KfVJi1RWlhUoZwH7+6hPCkEYSC86v9w3UGMmrUc76/zTVXd+dTEoB44uTtGnmpdfkRWEOU+USp0vkuYe/NZePjSfrrbEwilawxUmh3wvfzVLCl8xtTHFm7V3ziD4C1l3dqIphwb4Db7z/8Nx6jubfD8bwYDAIZ0yQYQOPZgUqtVLRArWxMLkkaJXuSuwHDx1WapRUQpgDbQbzUhwSYrLCpd//LSXXjiv9sEdw+JFIIwELvM1fHc4p3C52BdAVpjRvhnXSlnBm+d8I2mg2pOQMSdym/P7Cx8Tk0MHDTMd3CB8oIAwMJfj6hmaTUTvp0FuT6RYoQl5dxebfHhbSPRrTWwkA829nC+DJxiDlfU4erXV+Orzdqq0/5rxR4AVL8nmonUS/uJy/pLvtcpPHtqiaP5nEjy6ueuhmY0uz3Ydbwaf/9uN+b/vB8HT3t/oyRSCMJAzPCfJgUQKfJTKuUxWFpyHIDvZWeWyV/vPegietEfOFWH464GXD5HOotn7zOXMPf9VFYsMVzwekHsp+/bIUNh6+ARd9CsGTzXvLEG6/af9lvO4pTKDCAiehCXschLD1814I7Z0mn1FbXsMh1qlpT7LvROAhD3PYOeWIJBTyzBb15fjcMVvto+fF4oEikEYSBmPFCaLSmtnYOS7/iPH/8KwGdpMStTpfge9O+YGXD78/vkC5/v+WiTn/BY/McxivdVzS1kJrzQE78wCvPYMTehII5FGfPcCr/1ZZX1fst47hrXU/L9+W93KmxJRBPiKb43q2QpNpq2GdIEhlPfYRdOVfP28JZB1gCp+FAlTtX4hA+fRt/KgbOUcZaIOiIhUvwtKcr21ma3R8hOapZIEU8ZvHhgByQ77RjYauZlIa7CzHHAP5fvEb4P65aDPu2VhY48Pidc8KNFcdvNmHUh/j3pnZYtb8//AswQIqIDm82GnFQnKuqaMbJ7m8A7GMSQgmy8+NvB+NOnxQCA3SfYxSrVLCl8X6XUS67ed0r47Kr3uqdpCjJBGIgZIiWQu0cObykZ26ut37qih79BaXltUMfVivgOJNhsuHZ4F/TvqCxSAKAg12tKdtptkpdxSoA4FqWihGbDT490iv6/s0wIaAxlVs9F/dpLvtdGyOpEGM93fzoPX9wxGsO65YT1vFeJYswA4NDpOr9tFBLIAvCVb1Ayjny+0Vefq7HF+3ulZG4EYSBmxHkECsi0ycYlvCVFnFSJeVyzRIroHmjtX644w9v5uRqkU49TRJWBv7zz7NAbZxAcIyYlM8V4469c9OrJD5OTpiyaXvzt4KDbRESe3LREnNElx7C8PHoQz9JjuRvVLCkDOvKBs7aAM8z4wUpMxaSUl5ejsLAQ+/fvF5Zt3boVw4YNQ05ODu6//37ND/mCBQvQtWtXdOzYER9++KHephBxihmq32azaUpdv6bVVMqPVs5onb6qhNFJ3HjEz5hW0aZ028QzgoYUZPvliIhUSjc+JkXs7klPMt6SIn8JvatQfZmFmgidOKC94jqCUOO6Yb6M1qwZZ/zzP6QgG/eO74WnLh8grMsQZZoN9CrmA2etnNNHl0gpLy/HpEmTJAKlsbERl112Gc4880ysX78eJSUlmD9/fsBjbd26FTfccAMeffRRfPvtt3jsscewcycFnRGBUfKf/lZmJtVL3w7KcRn8aKbZzaG2sUVw96jtA5hnSRH3PdpFCnu7W0Z3k3x/6OI+2D/7Uk0BuWYiuHtEgbOs6q9G88oy5ZopcpIcdjz/m0HMdUpZgQkiEGN6+QZMfHCrGF63dMpOwT3ji5AtcoMG42KOGXfPtddei+uvv16y7JtvvkFVVRVeeukl9OjRA8888wzmzZsX8Fhvvvkmxo0bh6lTp2LgwIG466678O677+prPRGXKD1QGcmhjbInD+4IgF199+Apn1/Y1dDsC4y1J2DLExdh46MXMo9pVkyKxJKi8RSs2za8Wy6GFGRrPlc4Ybl7MpLNFymnZdM+82UzLuT89qwCvHnzWX7LjcyOS8QXfdpnYmAnr9uGL3Iqhn8m+XGH2P0TjHsqZgJn586dixkzZkiWFRcXY+TIkUhN9eZhGDRoEEpKSgIeq7i4GOeff77wffjw4diwYYPqPo2NjXC5XJI/Iv5Q8p+GarUY0CkLPz4wDotmjPFbJ+4EGpo9QkyK05GAjGQnctMScVG/duiUnYK3fud7YTm1KgidiHWDVksKS2t0yFYOiuUPGzF3jzC7x9zAWQD4+7VDFNcpVdXm81EA/laT8X3zIxLLQMQOvCv5uKvBb5289MIFfduhQ1ayUGuMR5x6QA2nha1+uoYlhYWFfstcLpdkuc1mg91uR0VFBXJylKOi5ftlZmbiyBH16XuzZs3Ck08+qafJRAyiNE000YAHTZzdVIzYLTzuhe9F5/SJkDduOhMeziuirjmrAG6OQ1YYCpVpFSnf7zrpt8zKAXN8WvwUpx3XDS9AY4sHnXNSAuwVHFOGdMI9H/2q2g454sRb8sJwWgvFEYQS7Vpn1Z1w+VtSfJW2vd/TkxxY9eD5ftbS8/vkY/mOEwHPpVQ93QqEbDt1OBxISpKaQ5OTk1FXV6cqUuT78fuoMXPmTNx3333Cd5fLhYKCApU9iFjkwCl2uvE1+7RlBQ0GJZeH2Hpjs9nA66RnFeIUDGuP6LPWATurDohaEidhRlOETCm8NrDbbJh1pbn3U70d7BsgvnNyFySJFCJU2rQKB7n7EfBZRcUDFNaA49phBSg+VIlPNxwG4K2OvOFABeNc6i7NSBKySMnNzcXWrdIiZNXV1UhMVFdmubm5OHnSN7LTsk9SUpKfICLiD6X6NGqZQUNF6UUVqbgDccS/VmvIvvLoqiXDC8Nw+cvH9W6LFTv9rU1aQnIcZEkhDCapNTVAE2Nw4RFiUtSfDYc9Ac//djB6t89AdUMLrhraGec+vwIFuSk4dNrXX1rZkhLykzRs2DCsXr1a+F5aWorGxkbk5ubq2m/Tpk3o1KlTqM0h4gClYNRbz+5m2jmVkicZUZU3GIKZ3cOaiaT2/vXFpETGlMK7WcIV2vGYqLibuKiikhXtvN6+RH5yn36iw7puNCI64NMXNDb7i5QWnc/G1DHdce+FvdClTSr2z74UPz5wvmS9WbFeRhByD3vuuefC5XLh7bffBgA888wzGD9+POx27w2urKyE2+2fhfGqq67CRx99hC1btqCmpgb/+Mc/MGHChFCbQ8QBSi/lW8/2j5kyCqtZUqSBs9r26dPevzjfeo2F8yKB2N0TDsTxLmKRIheob986DFufnIA26T6rrkMWIK1WpZYgtMAPxhoZlhT+ue3Whh1DpwXxrDUzkiQaRcg9rMPhwJtvvom77roLeXl5WLhwIZ599llhfU5ODrZs2eK33+DBg3HPPffgrLPOQqdOnWC323HHHXeE2hwiDmC9s2w2c4NAlUz+ERMpIuuG1lkkLHG3/5RyHBi/tfjaG5rdzDTdZiC4e8IkUpz2BOE3JA7OlluSahpa/PK1yC0pH0eocjQRO/Dxbo0iwdzs9mDm51sEt+SE/sEnDBRPbRYXVLQaQcknuflz8uTJ2Lt3LzZs2ICRI0eiTZs2ituKefrpp3HDDTegrKwMY8eODRiTQhAAO0+K2ebKG0d2xaItR9ElNxUHRS9ps5K1BSQID0yPfJ0VhFvFgfgR/s3rP2NrmQtf3DEaZ3RhB8YfdzVg7g/7MKZXW2ZtI624ufC6ewDv6LWuyS0xscstKTmp/v2UPCaFIEKFd/fUi0TKW6tK8eG6g8L3onb+1tFgsPJ0ecNsPO3bt8ell16qe79+/fqhX79+RjWDiAPsjNwjZucbG9WjDdbMvAAOuw1n/W2ZsDxS+QWCudzrhhXgVE0jRvfIw9VveOPB+KKDWtla5s1N9N/iI0yRsudEDca/tBIA8OaqUuyfrb9P4BHcPWGcJp3stKOuyY2GFnFMivffmRf3QYuHw9k9/aviyoXz5UM6mtpOIvbplpcKmw04cKoOR6vq0SErRSjLYQRF+enYfaIGt4zqatgxzcC6jiiCUKAfI117M8NvazTts7x5C5x2G5rd/nVlwkkwosxhT8Afx/eSLFOLnRDcPYx1TQq5an7aU654vKYWD6587SdkJDnx/tQRAWft8FOmw1lXRIgDEFlSeGvweb3z0ZsR1wP4J3O7Y1xPk1pIxAv5Gclol5GMY64GnHA1okNWCmobfeI5UHHTQHx8+yj8tKccF/VvF2pTTYVslETUwUrjrpTgzQze+/2IsJ1LiVBn3Fw60JuZ8vax3bWfU6SM3l97ELWNLX7byMWiOAB1X3kNtpa5sHrfKRQ98g1mfb1d9XynWvNDhHN6pCBSWsSBs4HdTvLAWSsXbCOih2Ot2WZrWp818c/s/gm9Qzp2bloiLhvc0bQiqEZBIoWISgrzpPV1lLKCmsGI7m3w1u/OwqfTRoXtnHJCvdxXrh2Cb+4Zg5tGKpt6hSnIrS9puRD886fFfvvUNUln8okTUa0r9c0kcns4vPHDPtU27jvpzevCZ94MB8mtAYQNYktK679qhh+528/KmXyJ6OOBBZuxYMNhX4JFxE8cFLl7iKgk0gPV8/tE1kTKmk6sB6c9IWAFZzlyK8k3W4/B4+EkbhuWSOHTxz+2cJvmc32w9iDKa7yzD9oGKPBnJLwlZfW+ctw4by0AseBQ/tElO+2w2cSZQM1sJRFvlFXW48+fFiNTVGAzXn5jJFKIqIQfZfNYPfjLaNplJmPpveci08RZTfKYFFb8ytrS0xjVwxdIKnbvAEBFndeSotfS9ZcvfGkLAlUhNhLe9D1nxV5hGd92tZdCstMeVNFHgtCDq8HnYq1v8s8/FouQSCGinnd/PxwjCv1nXMQ6Rk0/DAT/8m0WqRTeanDgVK1EpNQ1SeNUahpacNzVgBHPfBf0+ZWqEJsBn4qchTzuRA3SKITZJFs4t4mRxIdTi4hpxhS1jVy+khhGnjvhlrfWCZ8nD/ZOseUD+k7VNOJEdYOfu6e6sQU/7pbO+Fn14DgA3jgOueWFh485ukBjqXmjUAsiTE/WLpbIkkIYQccs5XisNhaut2Mk1LMTUcm1w6j6tdn4XrMcOI7DtiMuYQmfcbWqvhluD4cz/7YMw5/+Dl9tPio5xqmaJiz8tUz4Pq53W3TKToEjwTuN++o3Vksy2O445sL3O0+gtLUY4vO/HWzKtSmhZkkJlJVzxvm+acckmgkjeEHl9z+ye3xYj+lJIqISeV4KwlzEvnAAgnXkn8v3SEQIz7Bu3kRvzy7eIWybm5aIf998Fmw2m1AgbfPhKtz5wUYA3tiPia/8iN+9/QsAIC3RHvbqrMkqlpRAwuO3Z/mEc6g5LAgCALoo1Ob52+UDkBMnlhSKSSGiEj3xAURw+KYgAwdlNX6q6puFz/d94j8VOSvFvwP94o7RcDKmTW4+XIXqhmYcq2qQLK+NQGCgmiUl0LTizjkpuH5EFyTaE5CaSF0rETpKwjgtKX5EMPX0RFRC5vTwMu29DZLvd47robhtdqoTgztnSZYV5KagaxtfbpvnrhokWf/Kst0oOepCpEkK4Xdls9nwzBUD8cTk/ga2iIhn8jPYMSnpSebWKrMS1NMTUUmkaubEE3ziqJrGFpRV1gvL8zOSVGcWPX5ZP9w+1idifje6G3584HzJNlPO6IjuooR8i7ceQ7XMpZSpI1DVKOJlxgQRvdw4sgvOD3NAeSQhmyQRlXTI0lcYjwieBz/bLPneLS8N5/Vqi4cu7oPZ3+wA4J1p8Pjk/sjPSMKIwlzYbDase/gCfL/jJK4c2snvmEkOO5beNxavLNuFfy7fg7LKerz5ozQD7Z8uCi3tdzCEYkkhCDN45oqB+MsXW/D6jUMxcUCHSDcn7JBIIaKSyUM6YsGGw5g4oH2kmxK7tBqr5HnYnrtqEGw2G6aN7YFpY5XdPvkZybhaZRaWPcGGH0TTk/e3xr1cOqgD/u/sbjijwL/KstlYvY4JEX9cP6ILrhzaKW6tfCRSiKgkM9mJL+88O9LNiDtWzzzfUCvWpQPbo/hQpWRZfkYSzuyaa9g59JCsEjhLEJEiXgUKQDEpBEEoII/6ue/CXoa72W4e1Q3n9MyTLAuUj8RMyJJCENaCRApBEJowI14j2WnHny7q5bcsUlBMCkFYC3oiCYJg0iSretyis0igVuTTySOZqE8ukMb39Va7vmtcT9bmBEGYDMWkEATBZNPBSsl31iwdI5C7WNxuc8SQFuSWlBtGdsFjk/qhIJdmkxFEJCCRQhBEQB6d1M+0ad9yYTCkS7Yp59HC/lO1wucOWck4u0ceJQ4kiAhCTx9BEAEpzGPXEDGCzGRf9sx7x/fCmKK2pp0rENmpvnT+828dTgKFICIMPYEEQTDpLcoqe1Y386YEZ6X6RMronpGt7HrFGT6XVlF+egRbQhAEANg4joucAzhEXC4XsrKyUFVVhczMzEg3hyBiioOn6vD01yW4YURXnNvLXOvG1rIqbD/qwm/O7AybjUoeEESso/X9TSKFIAiCIIiwovX9Te4egiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsCYkUgiAIgiAsiSPSDQgFvoCzy+WKcEsIgiAIgtAK/97m3+NKRLVIqa6uBgAUFBREuCUEQRAEQeiluroaWVlZiuttXCAZY2E8Hg+OHDmCjIwM2Gw2w47rcrlQUFCAQ4cOITMz07DjWhW63tgn3q6Zrje2oeuNfjiOQ3V1NTp27IiEBOXIk6i2pCQkJKBz586mHT8zMzNmfhBaoOuNfeLtmul6Yxu63uhGzYLCQ4GzBEEQBEFYEhIpBEEQBEFYEhIpDJKSkvD4448jKSkp0k0JC3S9sU+8XTNdb2xD1xs/RHXgLEEQBEEQsQtZUgiCIAiCsCQkUgiCIAiCsCQkUgiCIAiCsCQkUgiCIAiCsCRxIVIWLlyI7t27w+FwYMiQIdi+fTsAYOvWrRg2bBhycnJw//33+9UQ2LNnD3Jzc/2Ot3LlSvTt2xd5eXl46aWXwnINejH6mv/973+jQ4cOcDqdGDt2LI4ePRqW69CK0dfL09zcjIEDB+L77783s/m6Met6r7nmGtx9992mtj0YjL7ep556Cu3atUN6ejomT56M8vLysFyHHoK5ZrXn1Or9ltHXG4t9lpZrsmqfFTRcjLNnzx4uJyeH+/jjj7ljx45xv/3tb7nRo0dzDQ0NXLdu3bjbb7+d27NnD3fJJZdwb731lrDf3r17uaKiIk5+i06cOMFlZmZyTz75JLdr1y5u6NCh3PLly8N9WaoYfc0//vgjl5+fzy1dupQ7dOgQN2bMGO76668P92UpYvT1ivnb3/7GAeBWrFgRhivRhlnXu2jRIi4/P5+rqKgI05Vow+jrXblyJde/f39ux44d3O7du7lLLrmEu+mmm8J9WaoEc81qz6nV+y2jrzcW+yyt12TFPisUYl6k/O9//+PeeOMN4fvy5cu5lJQU7osvvuBycnK42tpajuM47tdff+XOPvtsYbt+/fpxzz//vF8H9/LLL3N9+vThPB4Px3Ec9+WXX3I33HBDGK5EO0Zf81tvvcV98cUXku99+/Y19yJ0YPT18uzatYvLzs7munXrZqkH3ozrramp4bp27crNmzfP/AvQidHX+/zzz3P333+/8P29997jRo0aZfJV6COYa1Z7Tq3ebxl9vbHYZ2m5Jqv2WaEQ1bV7tDBp0iTJ9507d6KoqAjFxcUYOXIkUlNTAQCDBg1CSUmJsN1XX30Fm82G+++/X7J/cXExxo0bJxQ0HD58OB566CGTr0IfRl/zrbfeyjyeVTD6enluv/12PPTQQ/jmm2/Ma3wQmHG9Tz75JJqamuBwOLB06VJccMEFqkW/wonR19u/f3+88cYbmDZtGtLT0zFv3jxceOGF5l+IDoK5ZrXn1Or9ltHXG4t9lpZrsmqfFQrW6IXCRFNTE1588UVMmzYNLpcLhYWFwjqbzQa73Y6KigoAkKwTI98vMzMTR44cMbfhIWDENYs5ffq00MFbEaOu9+2330ZVVRX+/Oc/m97mUDDieg8cOIC///3vKCwsxL59+/Dggw/i8ssvh8fjCcs16MGI67344ovRo0cP9OjRA+3atUNNTY2lXthy9Fwzj/w5jaZ+y4jr1brOChh1vdHSZ+klrkTK448/jrS0NEydOhUOh8MvxXBycjLq6upUjyHfT8s+kcSIaxZz5513YvTo0bj44ouNbqohGHG9J0+exMyZM/HWW2/Bbreb2dyQMeJ633nnHbRr1w7fffcdnnjiCaxcuRKrVq3CsmXLzGx6UBhxvQsWLMDBgwdRUlKCEydOoH///rjxxhvNbHZIBHPN8uc0mvotI65X6zorYMT1RlOfpZeYd/fwLF++HHPmzMGaNWvgdDqRm5uLrVu3Sraprq5GYmKi6nFyc3Nx8uRJXftECqOumeedd97BihUrUFxcbEZzQ8ao6/3jH/+I3//+9xg8eLCZzQ0Zo6738OHDGD9+PJKTkwEAGRkZKCoqwp49e3DRRReZ1n69GHW977//PqZPn46+ffsCAF555RVkZ2ejsrIS2dnZZjU/KIK5ZtZzGi39llHXq2WdFTDqeqOlzwqKSAfFhIN9+/Zx+fn53H/+8x9h2Xfffcf16NFDsk1ycjLX0tIiLCstLfULups3bx53wQUXCN+XL1/OFRUVmdj64DDymjmO43755RcuIyPDssFYRl4vAC4jI4PLysrisrKyOLvdzqWlpXGzZs0y/0I0YuT1Pvnkk9y1114rfHe73VzHjh25L7/80sQr0IeR1zt58mTukUceEb7v2LGDA8CVl5ebeAX6CeaalZ7TaOi3jLzeQOusgJHXGw19VrDEvEipq6vj+vXrx/3hD3/gqqurhb+mpiaubdu2wvSuqVOncpMmTZLsy+rgTp48ySUnJ3NLly7lmpqauIkTJ3J33XVX2K5HC0Zf8/Hjx7n27dtzTz31lOR4VsHo6y0tLZX8jRgxgvvwww8tMzXX6OstKSnhUlNTuQULFnCHDh3iHnjgAa5t27ZcTU1N2K5JDaOv9/nnn+fy8/O51157jZs/fz43ZMgQbvTo0WG7Hi0Ec81qz6nV+y2jrzcW+yy1a7J6nxUKMS9SvvzySw6A319paSm3cOFCLjU1lWvTpg3Xtm1bbtu2bZJ9lawKr732Gud0OrmcnByusLCQO3bsWLguRxNGX/Mrr7zCPJ5VMOP/WMzYsWMtNRoz43oXLlzIDRo0iEtOTuYGDBjA/fzzz+G6nIAYfb0NDQ3cjBkzuI4dO3KJiYnc2LFjuX379oXzkgISzDUHek6t3G8Zfb2x2GfpuSar9VmhYOM4WYrGOOPYsWPYsGEDRo4ciTZt2mjer7S0FDt27MCYMWOQnp5uYguNJ9hrjlboemObeLteIP76rXj7P46361Uj7kUKQRAEQRDWJK6mIBMEQRAEET2QSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpKQSCEIgiAIwpL8P6+xGN2fO/sYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3539 entries, 0 to 3538\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3539 non-null   datetime64[ns]\n",
      " 1   open        3539 non-null   float64       \n",
      " 2   high        3539 non-null   float64       \n",
      " 3   low         3539 non-null   float64       \n",
      " 4   close       3539 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 138.4 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.580</td>\n",
       "      <td>11.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3535</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.592</td>\n",
       "      <td>11.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.73</td>\n",
       "      <td>11.624</td>\n",
       "      <td>11.662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.87</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.690</td>\n",
       "      <td>11.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>11.86</td>\n",
       "      <td>12.02</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.92</td>\n",
       "      <td>11.744</td>\n",
       "      <td>11.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3534 2024-12-19  11.59  11.64  11.54  11.59  11.580  11.660\n",
       "3535 2024-12-20  11.59  11.70  11.58  11.62  11.592  11.656\n",
       "3536 2024-12-23  11.64  11.84  11.64  11.73  11.624  11.662\n",
       "3537 2024-12-24  11.72  11.87  11.72  11.86  11.690  11.669\n",
       "3538 2024-12-25  11.86  12.02  11.84  11.92  11.744  11.688"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HwKuvvjooYxg7diwf//jHeeCBB/B9n8svv3xQrisiI9utt96KMYZTTz01v+273/0uxhg++clP5rfdddddGGP461//WnKNq666CmNM0eOYY47ZHcMfVCoRICIiIiIiIrIHPP3005x//vlMnjyZu+66q6Tx1bp165g9e3bJedu3bwdypQUG0/z585k9ezarVq0a1OuKyMj2/PPP579euXJlyf6HHnoo/+dxxx1XtO+f//wnF1xwAZ/5zGfy26qrq4dopENHGawiIiIiIiIiu9nq1as59dRTCcOQu+66i/33379o/9e+9jXmzJnDCy+8UHLuvffeC8CiRYv69dqnnXYa8+bNIwzDou1hGNLQ0EAikejXdUVkdNq4cSMNDQ1AcbC1w0MPPUQ0GuXBBx8s2ffcc8/x7ne/m0MPPTT/OOigg4Z6yINutwZYwzAkCILd+ZIiIiIiIiIiw8r69es54YQT2L59Oz//+c85+uijS475wAc+AMCXvvSlokzVZ599liuuuAKAT3/60/16/cMOO4xXXnmFL3/5y1hr89t/9KMfsWPHDk455ZR+XVdEesdaS5hqG3aPwp8HvTV9+nT22Wcfnn/+ebLZLKtWreLwww/P73/ttddYt24dX/ziF3n22WfZuXNnft9bb73FW2+9xaGHHjoof6970pCXCFi7di133303r7zyCslkEsgV6z7++OM57bTTiEajRcdv3LiRSy65pMdrfvSjH+WMM84YtDFu2bKF3/3udzz//PO0trYybdo0Tj311JK05a5efPFF7rnnHtauXUsYhsydO5ePfexjzJkzZ9DGJiIiIiIiIiPLZz/7WbZs2cKsWbN45ZVXSmqezpw5k09/+tN86lOf4he/+AWzZ89m8eLFNDY28tRTTxGGIV/5yld4z3vekz+noaGB73znO4wfP57/+I//6PH1L7nkEu6//36+853vsHz5co455hjWr1/Pfffdx4EHHsi3vvWtIXnfIpJj0yma//DLPT2MEtVnnIOJV/T5vIULF7Jy5UrGjx+P4zjMnTs3v++hhx6ioqKCSy+9lBtuuIFHHnkkH9N77rnncF2Xs88+m5UrVzJhwgQ++clPcuWVVxKJRAbtfe0OQxpgffTRR7npppsIw5BYLMaBBx5IU1MTW7du5c4772TlypUlf2lr164FwHEcXNcte93utvfHa6+9xte+9jXa2toAMMbw+uuv86Mf/YgtW7Zw1llnlT3v4Ycf5qc//Wk+um+M4YUXXmDVqlVcdtllLFmyZNDGKCIiIiIiIiNAbS0Ajz72GJDLZL3mmmtKDjv++OP59Kc/zc9+9jMOPfRQfvzjH/PII48wduxYTj31VD7/+c8XBVchF2C95pprmD59+i4DrBUVFfzlL3/h+uuv54477uBnP/sZBxxwAFdeeSWXXHLJXln/UET2nIULF/L8888zfvx45s2bVxS3e+ihhzjqqKOYPHkyS5Ys4cEHH8wHWJ955hmi0Sinn3463/jGN3jmmWe48sorAfj617++R95Lfw1ZgHXDhg389Kc/JQxDTjnlFM466yzi8TjWWv785z9zyy23sHr1au69996ibNSOAOvZZ5/NaaedNlTDAyCZTPLNb36TtrY2Jk+ezOc+9zkOPvhg1q9fz//7f/+PP/zhDyxZsqQo8g6watUqfvazn2GtZdGiRXzmM59h0qRJ/P3vf+fGG2/kxhtv5IYbbqCqqmpIxy8iIiIiIiJ7n8zxx8Py5bs8znVdPve5z/G5z31ul8fOmDGjT8t7q6urufrqq7n66qt7fQ7kPuuLiBRauHAhP/7xjxk/fjwLFy7Mbw/DkEceeYR///d/B6C2tpa77rorv/+CCy7g4x//OAceeCAA7373u2ltbeXmm2/mv/7rv3Ccvad11JCN9Fe/+hW+73PaaadxzjnnEI/HgVym5/ve9758fYUnnnii6LxXX30VoGynxMF2991309jYSDQa5atf/Srz58/HcRzmzJnD5z//eay1/OIXvyg575e//CVhGLL//vtz2WWXMXXqVFzX5ZhjjuHDH/4wO3fu5O677x7y8YuIiIiIiMheorYW6upyDxGREWThwoW89NJLrFixoqj53j//+U/q6+u54oorMMbw3e9+N1+TFWDatGn54GqHo48+mu3bt7N58+bd+h4GakgyWFOpFNFolNmzZ3PmmWeWPWbOnDklxW3b2trYtGkTjuMMeYA1CAIeeeQRAE488UT22Wefov3z5s1j9uzZrFu3jrfffpuJEycCuSUcHf8QzjrrrJIasu9973u55557ePrpp/n4xz8+pO9BRERERERERET2LiYWp/qMc/b0MEqYWLxf582fP590Os1f/vIXLr30Ul544QUgVx5gzJgxPNZeFiWTyXDUUUfx4IMPcuGFF/LMM8+w3377MW3atPy1duzYAVDU3G9vMCQB1ng8zqWXXtrjMfX19QDEYrH8trVr12KtZdasWfmM16GyceNGWlpaADjqqKPKHrN06VLWrVtHXV1dvr7Nyy+/DEA0GuWd73xnyTnV1dXMmTOHl19+mTfffLPoH4mIiIiIiIiMUnV10N74ebDV3lab/3r5sl2XHhCRPcsY069mUsNVPB5nzpw5rF69uqhEQEf91cI+RUuXLuWhhx7iwgsv5Itf/CLHH3881113XX7/L3/5SyZNmrRbVrYPpiFtctWdMAx59tlnAViwYEF++5o1a4Bcg6tvf/vbrFu3jmQyyZgxY5g3bx7ve9/7Suqh9teWLVvyr9Xd/2nTp08HKEpL7jhv1qxZeF75v74DDjhAAVYRERERERHZPVbUdX69bI+NQkRGsYULF7Jjx478CvHW1laefPJJrrrqqqLjjjvuOG655RaCIOALX/gCn/zkJ3Fdl9mzZ3PPPffw4IMP8uMf/3ivqr8KeyjA+sgjj+QzWD/4wQ/mt3cEWNeuXUskEuGAAw5g2rRpbNq0iccff5wnnniCj33sY3zoQx8a8Biam5sBmDRpEpFIpOwxY8eOBWD79u35bcn2O45dSwqUO2/btm19GlNHGnRPxo0bV9SNTUREREREREREZE/qCLB2eOyxx8hkMhxzzDFFxx133HHccMMNPPPMM/zrv/4rO3fu5Prrr2fbtm0sWLCA3/zmN/zLv/zL7h7+gO32AOvOnTv59a9/DcAJJ5zAzJkzgVxWa0eA9fjjj+cTn/gEY8aMASCbzXLXXXfxhz/8gV//+tfMnz9/wJmsvu8DUFVV1e0xHfsK68Rms9len9fQ0NCnMV144YW7PObGG29kwoQJfbquiIiIiIiIjA4qFyAiu8snP/lJPvnJTwJw5ZVXcuWVVwJw6623dnvO6aefjrU2//zzn/88n//854dymLvFbg2whmHIj370I5LJJJMmTWLZss61C9ZaLrvsMlpbW3nnO9+JMSa/LxKJ8NGPfpS33nqLJ598kvvuu4//+I//GNBYOlKNC2vAdtVRAiCTyQz4PBERERERERmlamtz9VeDAIZiReIQ1XYVEZHe2a0B1jvuuIMXXniBSCTCRRddVJQF6rou8+fP7/H8d7/73Tz55JM8//zzWGuLgrB9FY1GAbqtowrkr18YKO3NeR1B2HQ63acx3Xjjjbs8Zty4cX26poiIiIiIiAwDQQDWUvuvPoyrg9tqBz3D9IY/BVycqMs9WbpkUK8tIiLd220B1ieffJK7774bgM985jMceOCBfb5Gx9L4trY2mpqa8rVO+6MjuNtRC7aclpYWgKLU5d6c11GntfC83tDSfxERERERkVFgkDNOk24AQEDfPoOKiMjg2C0tudasWcNPfvITIFdroba2tuxx1tp8bdRympqa8l8PJHsVYOLEiUCuEVV3gdDGxkYA4vF42fO6U+48ERERERERGd3ee47htYku/5zU/efeAUsmc48VdUP3GiIiUmTIA6ybNm3iuuuuI5PJcMQRR3D22WeXPe7BBx/kvPPO47777uv2Wi+++CIAiUSC6urqAY1r//33JxKJkM1m2bRpU9ljOppudQRVAWbNmgXAa6+91m1gdu3atSXniYiIiIiIyOjlV0ZZ6E7hjIbJnNRcA31c8SgiIsPXkAZYN27cyNVXX01zczPz58/nC1/4QreZp4lEgqamJh5++GGy2WzJ/mQyyZ///GcADjvssAFnsEajUQ4++GAgV76gnBdeeAGAmTNn5rcddNBBxGIxmpqa8vsLtbS0sG7dOqAzGCsiIiIiIiKjW6qmmqjNfY6dkYoxqUUBVhGRkWLIAqwNDQ18/etfp7GxkQMOOIDLLruMSCTS7fGHH344U6ZMYdu2bdx4442kUqn8vi1btvBf//VfNDY2UllZyYc//OFBGePxxx8PwAMPPMCOHTuK9r344ov5TNR3vvOd+e2RSISjjz4agN/+9rcEQVB03v333082myWRSDB37txBGaeIiIiIiIjs3YJ4tOj5rPqgmyP7LhG4JAIXl4ElIomISP8MWZOr++67L1+LdPPmzZx33nndHvvVr36V+fPn86UvfYlrrrmGxx9/nH/84x9MmzYN3/d54403sNYyYcIELrroIqZOnVp0/vbt27nooosAuOCCCzjuuON6NcYjjzySO++8k23btvH1r3+dz372s8yaNYu6ujpuuukmAObNm1eSiXrKKafw2GOP8eqrr3L99dfzqU99iurqah588EHuuusuAN73vvfhebuth5iISFm1t3XWvB7sLrUiIiIiMgzU1rLuUMu+mSitagMiIrJHDFkEcMOGDfmvgyAoyfQs1FHLdM6cOXznO9/hrrvu4tlnn2Xjxo1UVlYyf/58DjvsME488cSyjaOstfmyAmEY9nqMkUiEiy++mK9//eu8+eabXHnllUX7E4kEF1xwQcl5++23H+eddx4333wzzz33HM8991zR/tmzZ/OhD32o1+MQERERERGR0cUZpAoBASFn1U8iEhqqayw1QSP1bmnZPRERGTrGdtepaRTZtm0bt912G88++2w+2Lto0SI+85nPlGTLFnrppZf45S9/yWuvvQaA53mccMIJnHPOOWUDwSIiu5syWEVERET2PFt7Ak2b13Ld0Za0m9sWO2AW37zy8QFfu+09x3L1/rk+IB97Ee6ck+GvFTtoixj+cXPvE5BERPrq1ltv5VOf+hSnnHJKvmn9d7/7XS655BKWLVvGrbfeCsBdd93FGWecwWOPPdbtqvPLL7+cN954I39OhzAMueqqq/jZz36G67p8+ctf5otf/OJQvq1+0Rp2YPLkyVx66aU0NTWxbds2JkyYwPjx43d53jve8Q6uu+46tm3bRnNzM9OmTaOiomI3jFhERERERET2Frbgfzu83vD6oFw7s20z7N/5fGIQ7f5gEZEh8Pzzz+e/XrlyZcn+hx56KP9nuQDrb37zG775zW9yzjnnlOz72te+xvXXX8/111/PvvvuywUXXMCUKVP46Ec/OojvYOAUYC0wZswYxowZ0+fzJk+ezOTJk4dgRCIiIiIiIrLXe+EFqIkBcE577CEMMoP+MlOSYCw4SlwVkd1o48aNNDQ0MG7cuKJga4eHHnqIaDTKgw8+yNe//vWifTfffDOXXHIJ8+fPLzmvqamJ66+/nquvvpovfOELADQ0NHD11VcPuwCrs6cHICIiIiIiIjKShV7pR2/HdQf1NTJuZ47s6S1TmZPW6kqR4cpaS1smGHaP/lQRnT59Ovvssw/PP/882WyWVatWcfjhh+f3v/baa6xbt44vfvGLPPvss+zcubPo/Keffpq//OUvHHrooSXXfuKJJ0ilUvzrv/5rftuHPvQhXn75Zd58880+j3UoKYNVRGQEq9tSt6eHICIiIjLqBdHOj96BcbAYosZiwxDjDDzvaUoLbK2CnZVgDRgMS1NjCNMpnJj6g4gMN6lsyP/8Y3DKhAymTxw2nYpo32/+LFy4kJUrVzJ+/Hgcx2Hu3Ln5fQ899BAVFRVceuml3HDDDTzyyCOcccYZ+f3//d//jeM43HjjjSXX3bx5MzU1Ney77775bTU1NYwdO5ZXX32VadOm9XmsQ0UZrCIiIiIiIiJDyJrcn22RCfxl7vv44/zTeX7qImx2cMoEOMYhcCpYMW0JG8cfRlN8HxwMwbYtg3J9EZGeLFy4kOeff56VK1cyb9483IIM/YceeoijjjqKyZMns2TJEh588MGic50ebjK1tbUxbty4ku2JRILt27cP2vgHgwKsIiIiIiIiIkPJ5CKs9VULSHm5jNL1NbOpb2wd0GULl/O+nVjMazWzaKjYnw01R5KMTgbHDOj6IiK90ZHB+vzzz7Nw4cL89jAMeeSRRzjhhBMAqK2tzTe86o1YLFYUrO1gjKGtrW3A4x5MCrCKiIiIiIiIDCFrDIExpCITira//nbzwC7sZ3PXx5CMTQUgGc091k84CMK+11MUEemrhQsX8tJLL7FixQoWLVqU3/7Pf/6T+vp6rrjiCowxfPe7383XZO2NyZMns3nz5pLt9fX1VFVVDdr4B4NqsIqIiIiIiIgMJQPNsUTJZif0B3RZm0oB0OLF6Zo/lYpMIhykEgQiMrjiEYdPHDZ9Tw+jRDzSvzzM+fPnk06n+ctf/sKll17KCy+8AOTKA4wZM4bHHnsMgEwmw1FHHcWDDz7IhRdeuMvrLl68mNbWVp577jkOOeQQAFatWkVra+uwqr8KCrDKMFZ7W23+6+XLlu/BkYiIiIiIiPSfNYbWSHGzKSe0WL8fAdbazs9J9g+/AyDjls/kamxNM7nvryAiQ8wY069mUsNVPB5nzpw5rF69uqhEQEf91SVLluS3LV26lIceeqhXAdaZM2dyyCGHcO2113LnnXcC8P3vf5/x48fzzne+c9Dfx0CoRICIiIiIiIjIUDKQ8mJ0XbCfyfYtwFp7Wy0rttSxYksdQL5J1ptjlxKUKbfa3KYMVhHZPRYuXMjEiRPZZ599AGhtbeXJJ5/k2GOPLTruuOOOY/ny5QRB0Kvrfuc73+Huu++mtraWD3zgA9x8881cccUVeN7wyhlVgFVEZARbMnVJ/iEiIiIie4Y1hlQkBsC6mtxjTQ2k/XBg181maIxWkfHKZ7AmU9kBXV9EpLcWLlxYlL362GOPkclkOOaYY4qOO+6442hoaOCZZ57p1XVra2v561//SiwWY8eOHfz3f/83//7v/z6oYx8MwyvcKyIiIiIiIjLSGENbpKJkc7qPGaxdWd9n9cQZuBgoyY+F5jYFWEVk6Hzyk5/kk5/8JABXXnklV155JQC33nprt+ecfvrpWFv686qnc4488kgeeOCBgQx1yCmDVURkhKvbUkfdlrqiusYiIiIisvtYAw3xMSS6rNhPZQeWwfrZa49hfaKa2TssrgVjoSKbexjPI5keWABXRER6RwFWGdYUGBIRERERkb2eMeysKF3G35LtXQ3CEskk1NXhWUNTrIKgm0/2zZl+Xl9ERPpEJQJk+FpRB4lk7uupe3QkIiIiIiIi/WYNZF2Phdugbkrn9lR/AqzJJOe/3yeRbWR6KiDjxbo9tDEdYK3FmDIdsEREZNAowCoiMsIlM8k9PQQRERGR0c0YmmMemS6ZppmgfyUCAgeSEYuTiRH2sDA15Ye0ZgOqovroLyIylPRTVoan2lqYneTeu30whkRFHSzb04MS2fvUbanDD3O1tx7f+PgeHo2IiIjI6BQaQ9aJdG5oTygNgpDQWpx+Zpg6pqKot5W1FmMt1rQHXYOQnS0ZBVhFRIaYarCKiIxgc9Y34obgDqx/goiIiIgMQNbtJsBpLX5Q2k27O3Vb6gjCzrICjlOR/zowsKomTWgaaGuP5dowoL41268xi4hI7ynAKsNWYHMTB2t7P+EQkWI//ZOb7yQrIiIiIruftbbbAKsNQ7Jh7+6E195WSzKTxBakrLqmM8DqWjhxfRsTW5MkMu0bw5Bkxu/32EVEpHe0TkCGr3KB1dpaahfX5b5euoTly5bv1iGJiIiIiIj0SRiQ9SLl9/Uxg7Wr96+r4IX9O5/H021E/bbOy4cBLWkFWEVEhpoyWEVERonC5WQiIiIispsEAVmnOLepzcs97lp1F9l+NLqamI1wy8OVZONVRdvjqVYiYWeAlTBUgFVEZDdQBqsMW641gKWo3HtdHcxWR3QREREREdk7WN/Plwjo2srKseCHfctg9eIVHGLHQQ2k3VjRvvpIKyEZwoIXymQyiIjI0FIGqwx7FkhmFFQVEREREZG9UJkM1g4G8PuQwfrQLQH7RsdhLATGYUPNzKL90XQLji3OWM1mtYpJRGSoKYNVhq39/QrGTKwGoLU1F2CtPSNJ3aTcBGHJnhqYyF5kaTIBNADgOu4eHYuIiIjIaGTDoNsmV33NYI1EY0Ta46UvT1lQst/LtOJYjwPrIXQCqF9JZl7pcSIiMriUwSrD0nsW1THPH4NxHBzHYWwsgQ1DAhtg2/8Tkd4xlC5HExEREZHdxPd7yGA1ZHvZ5KpuSx2O62KBM1dF2FI9p+SYi85w2VrlEzqds79M1seWayAsIiKDRgFWGZbG+C4R2zkpMK5L2NQAwJwduQcr6vbI2ET2NsbmHiIiIiKy+9keSgTkMlh7XyLAM7kVSZvGHkBbJLctdHKPV+NthLQQUFBzNZnE/u53+O9+T7/HLyIiu6YSATIsVYS52P8D+Zuyln9v3AnWUhW6OFZ1WUV6w495LNs+hYrQ4e81bbs+QUREREQGV+B3WyLAQK8zWAGM42CB12pmlexLOyEhrWSJFm23QNqNEOnDkEVEpG8UYJVhaaIfgS5lAGwmw4GZKg72xwCwNkztgZGJ7F1SYyuoDHONDg5vqiJMNuMkqvfwqERERERGDxv4+AVJqnPrYc2E3NepWN8yWI1xeHXiDJKxapwucdm2cCuWDL6xODYkNJ0LVlvdKImBvAkREemRSgTIsDTOd/EK5hn1FfD5e8/n4Gw1OyphRyXUmAqC9rIBIlJeECu+j5Z9fd0eGomIiIjI6GTPOYesKZ/bdOjGgOy//wfU1vbuYo7h+X0Oyj+taq8GMHvHq7ztLwcga0LifmcyijXQ+ur63Gv09nVERKRPFGCVYSlqDWe/UFw7sipDvi5r1oGsY/n8zafuwVGKDG828Es3GrW7EhEREdndMgUlAtZMgGQ09wDwnd5/LB+XqSYZqwKgMpsrMXDYpqc46LWnmbMzzZKpSwhcQzTbhrWWwAaApTUSH8R3IyIiXSnAKsOS1x5I9Z0YKW8MH33R4by7Xi9p1OP2fjWNyKiT3fBq6UZPlWFEREREdidrwO+myRXG4Lc3ruqN+kRN0fOYn+axA97ko+9N8sK03HUyxjI12UYiA25g+XP1NlZWB6zYUtfftyAiIrugT9oyLEVSWd6qnsTKaUcQOhEqwyQnbHoGaCg6bvPb6/fI+ET2Bpn1a0q2mW4aLIiIiIjIULE9N7lyehlgtdBUMYa2gkvVJHdy03717Kjo3JY1lng23fka1pKKVKhJsIjIEFIGqww7NghwrGXV5LlU+hESGWiJJnh5yvySYysD/RMW6U6wfWvpxj40URARERGRgbPr1pF1PdbVwKs10BI1dPwH4PcywJpJt5CMVxVtm7T5TV6L54KpiWiC5cuWM3fKfCqybfljHGtIRSsH6d2IiEg5ik7JsGOzWQC2JiYVbf/LAVNpiI8v2lYRqp6kSHfcSVOKnn+iznLhd2upvWg8tbepwYGIiIjI7mAdQ7aHEgFZ07uP5QtbKsi4kaJtFanWkuN8ByozKV6cDIGTy5JNRStKjhMRkcGjtaIy7Nj25SxhmWLvW8csZHLzS6yfejwACX/lbh2byN7Efvs6yGQwNlf7C8CxPZ8jIiIiIoPLOoZse2DUAK7jEoRBfn9vm1zVZL3iUgPWEtr2TlfViYLrQUU2lX/uYMhEKqhIBYiIyNBQgFWGn2yW0Lh05KbOqc/dec3VGprIa9Hj84c2x9/J9mSKSQl1xRTpjdcqUtQlWkBNDkRERER2i9Bxuq3BCr2vwTo21bVZlsEL/JLjfMdQEXTWYHWAdCQOIVBX17tBi4hInyjAKsOOzaTxnSiRbvbPrYc1NWCMoc2D17Y2KsAqUka5ZNX9d4Y0tgVQryYHIiIiIruD77hYui9tljW9C7BWBU5RgNVpTeEFPou2gnvwkvz2oEsGa1XoYaIx0tEKSCEiIkNANVhl2LHZLIET6/Xxb+5oHsLRiOzNikOsxlocC9ZaAqslYiIiIiK7Q9brLnUkx+9FDVZrLWMCD78wEzYMiQTZkpvqvgOxbApjQ5z2/qZuLIY/tqaPIxcRkd5SgFWGHZtJE5jSAOvCbeCGYLrMIPxMdjeNTGQv89ZbEBZ/w7g9ZE+IiIiIyOBLB2G3+6rSluz2t3e5dN8mm4law864x+qJudvoJgjLlgj4xRm342BYsC2FgfzsLxg/ud/vQUREeqYAqww7NpshcGK0eRCY4hy8Bdsg7ucmCW3x3FKallatcxHpLaej25VVtysRERGR3cHfVQar65Yt7VQobGuhps3gd9RrtRasJRJkMab4BrqJ5F6vItuGpfPz1AMHjqH2jCS1t9X2/U2IiEiPVINVhh2bzeC7MTaOhbYI7CrhrjWVIQgtrqPMPJFCNpNmSwKS0TE0x2ezalIKw8rcvj08NhEREZHRIuN2H2B9cTJs8DL8dnySR3u4hs3mSgEE7TVYnTBX1TUSBvzxty7VP1+eP9Z4EXaOjzEutZOtiQmd4/DGQzqAFXWwbEBvSUREulAGqww7NpPhxA0xFm81JDK5bU4P0SAbBLRlVU9SpJC1FshlOWwZezzN8ZmsnjyP5uqle3poIiIiIqNHbS1ZtzivyXSphW8AzC5ynwKfjvyo2fVQmc2dGQnKlEtzPf589FQS6YaizSlvDJ5VUoqIyFBQgFWGn0yaVGQXTa4Kl8GEAcl0ae0hkVEtzNX62jjuAELTmTXRWjEboGQpmYiIiIgMja4BVjdMFz3PzcrcHq9hs1k8Oud0xkJ9leH18ZZXD0gUXy+Se73qdK4Z8Jx6qPAh4yU4IBjXr/cgIiI9260B1jAMCYK+ZRr25xzZu1k/S9qLArnJRmhgzYTulzTbIKA1o38jIkWsBcews2J80ebq0OMjTdN4X/NEgubGPTQ4ERERkdHBYsm012BdvCX3qInGi44xmF1msNrAJzRevmlVx61yr0wGq4nFCQ1Up5tK9sUqj+jP2xARkV0Y8hqsa9eu5e677+aVV14hmUwCMGXKFI4//nhOO+00otFoyTl1dXXcd999vPrqq7S1tWGMYd999+Xkk0/m5JNPxnEGLy78wAMPcMstt/R4zNVXX83BBx9csn39+vX84Q9/YNWqVaTTaWbOnMkZZ5zB0qVagjsQNpshNXkfiI9lcQqabUPJMa2eJQjbs1bDkJaMMlhFioS5mw6BU5oNEThxxgWQeXEFFUeesJsHJiIiIjI61N5Wyw1bVpKccmjRds9mMLjY9nwnY8HuqkSA7xM4Lm7Yfo1xE3CatuJUVZYcalyPppjBC0uTUHy3qn9vRkREejSkAdZHH32Um266iTAMicViHHjggTQ1NbF161buvPNOVq5cyZVXXkkk0rnU4Q9/+AN33HEHAJWVlRx00EFs376dN954g1tuuYWXX36Ziy++eNDGuGbNGgBc1+02cFtuKe2KFSv49re/nc+uNcawZs0arr32Wi644AJOPPHEQRvjaGN9n5RbHHj3ghTQeafXsVlC2o8JAlpUIkCkqCPswx/9EwBZJ1LSKC7rxvH8FJn1qxVgFRERERkqK+r4t2ObuGRTcRDUoxVjK7Em9/nTATC7KBHgZwkLgrAG2L9yEodMXVL2+Fcn5q43JtVMU7waCySj0FRRw3Mm1c83JCIi3RmyAOuGDRv46U9/ShiGnHLKKZx11lnE43Gstfz5z3/mlltuYfXq1dx7772cccYZQC5oeccdd+A4DmeffTYf+MAH8DyPIAi44447uOeee3jqqaf461//ynHHHTco43z11VcB+MY3vsHs2bN7dc6WLVu44YYbCIKAmTNncuGFF3LAAQfwwgsv8L3vfY9f/OIXLFiwgMmTJw/KGEcdP0v6sCOgvQB7+NeHiDU/yYItE3m7ahJTmt9ialOCP8+dC8DfXv8r8w5dvCdHLDL8tNdgbY0WT+jbIrA9UUU8UHkAERERkaEWsYbWSEXnBmMwThpjo9BeJ783JQIIfE5fX8HDM9uPO6GWyEMPdnv4bV98jBd+Mo5jM//gN0tP5MX2j6YGQ9w7aCBvSUREyhiyGqy/+tWv8H2f0047jXPOOYd4PJd9aIzhfe97H4cemlsm8cQTTwC5jte//OUvATj33HM57bTT8LzcLw/XdTn77LOZMWNG0TkD1dzczJYtW/A8jwMOOKDX5/36178mnU4zduxYLr/8cmbMmIHjOCxevJhPfepTpNNpbr/99kEZ42jkZ33SBd0twyDkmfhbTF73NItfeYh933yZeLYtvz/Z1khSJQJEirUHWFNePFfAuL2IsQGy7niibml5FhEREREZRMkkkdCQdSNFmwMni2MLlu8bCJwIPbG+j+8UBGFdh0iZEgD5SxpDJtnMtA0bGNfWkN/uABFnal/ehYiI9MKQBFhTqRTRaJTZs2dz5plnlj1mzpw5AOzcuROAHTt2MHnyZBYtWsRJJ51U9pxZs2YVnTNQa9euBWDmzJlFZQp60tjYyNNPPw3A6aefTnV1ddH+o48+mvHjx7NixQoymcygjHO0yfhdJwqW7/0pTbwlQ6S+mUhLmkq/M8DqWmhRkyuRItbmAqxFE/F2zbGJZIIMt9Xdhm0PxIqIiIjI4ItYQ9bx8g17zdixWBOA9bHtW3dWwJqJblG5p65yAdaCMgLGKVtjtVCrZ2nzLHO3v5Lf5lqDa8b19+2IiEg3hqREQDwe59JLL+3xmPr6egBisRgAEydO5Ctf+UqP53QEVjvOGaiO+qvz5s3r9TmrV6/G2twvwiOOKO3A6LouixYt4rHHHmPVqlUsXqyl633VesutsP+7ck8WL2Z8m2XBlhTr25uh2zCkOtVZN8i1hpa0j7W2bL1ckVEpDAmMQ9he22vxVkhk4OhNhpcnVXDA2FzWvs1mMLF4T1cSERERkX6KWKckgxUTYAoyWI01mF19NO+SwWqcnjNYO6yusVRkW/PPHcCYqD47iYgMsiFtctWdMAx59tlnAViwYEGvzmlpaeHFF18EYOHChYMyjo4A69atW7nqqqt44403SKVSTJw4kaVLl3LKKacwceLEonO2bNkCwIQJE0r2dZg+fToAmzdv7lOAdceOHbs8Zty4cbhuzwXQ93bxIMthO9aQdqKkogeT3fpacZMea4kGaea2/3WFJiC0lsBaPE0SRHLCkGzBJDyRob3rrKUh5rKxcWNuR6DsbxEREZGh4mEIjVf0ccbgY2yuxFng5D7q2F3UYLV+lmxhBqvjELG9WIlkLdGgc2VlNHQwGDJBSMwb2Z8rRUR2pz0SYH3kkUfyGawf/OAHe3XOvffeSzabJRKJdFtCoC/CMMw3uHr66adJJBLsv//+hGHIa6+9xp/+9CcefvhhvvzlLxcFgZubmwHYZ599ur32mDFjANi+fXufxnThhRfu8pgbb7yRCRMm9Om6exMbhlSEGeY3vpHbEG/Bvvw4TmiLjnEL79Yagw0tfmDxhqyqsMgwV1sLi+tyXy9dAmGI75b/ER+a3DI1A/larSIiIiIy+OI2RmicfIDVWIN1fAydPSQMYMwugp2+Xzy3cxwiV14BB/XcWNnakEhQXLquxvdI+wqwiogMpt0eYN25cye//vWvATjhhBOYOXPmLs/ZuHEj9913HwAf/vCHGT9+/IDHsWHDBlKpFMYYPvGJT/De9743X4e1qamJn/zkJzz33HN873vf44c//CEVFbnOj9lsFoCqqqpur51IJIDBqxU7qgSlzaqMtUXPrbVEKN5G4OOHXbaJjGI2DIqzHIoYLC6GANuLpWUiIiIi0ndJNyAWFJe3MwAmwLGdn3scDA4VPV7LBn7R3M44Dp6769V7YRjihT6GkI4WLDPScTK+brKLiAym3RpgDcOQH/3oRySTSSZNmsSyZct2eU46neb73/8+vu8ze/ZsPvShDw3KWCZPnszll19ONBrl4IMPLto3ZswYLrnkEr70pS+xfft2HnvsMd73vvcB4Di5X0o91YH1vNxfa1+bXN144427PGbcuHF9uubexvqlAVa6Bk7DkIcOcgja5xOGXB1JBVhl1EsmO7+2tmyDqw6hieS61yqDVURERGTIxGxxrftcgNXHC1ry2xwLrjMGKPNZqJ31s8VzO8ch4u56+Z4Nw9xr2iwV2RiYXBZtSgFWEZFBtVsDrHfccQcvvPACkUiEiy66qMcs0A4333wzmzZtIpFIcNFFF+WDlwOVSCRYtGhRt/s9z+PYY4/lrrvuoq6uLh9gjUaj+f3d6SgW3tcA60he+t9bp97+AW6oz9WGnJrJ/V2vnQBYA9bitMdQM07A+vEh1jiAZWI2gx9okiCjWF0dzGjPRl1RBycH+U6zbpl7D9Z4YFGAVURERGSIREPDPkF10TYvDAkNRILmou2OqaZuy/LuLxYEpQFWp3f9JyIhuGEG3+lMEkr7WsUkIjKYdlvFyieffJK7774bgM985jMceOCBuzznf//3f3n88cdxHIeLLrqIKVOmDPUwi3QEPLdu3Zrf1rH8v6OGbDktLbm7kdYqo7Kv3NAyviX3oCNAXebvscUNcWzA3B0w920Ib/2FMlhFClhrCbotEQCt0am541QiQERERGRIHJipImxvXtXxSSX2kY8QGvDC1vxxU7JRDsiOw+vpvnfX8k+mdxmsAEHWxw2Lk39Sme6zZUVEpO92S4B1zZo1/OQnPwHg9NNPp7a2dpfnPP300/lareeee26P2ab95ft+j0HQpqYmoDMjFWDixIkAbNu2rdvzGhoaAIjH490eI+V1nVQY18MdMxZ3zNii7dsi2XznTQDrOMpgFSkUBgQ9NEvYWbmQnRXzlcEqIiIiMkRmZisIupRsisajfPu938ENU0Xbp2UqWdJUnO1ayIZhUQarMWaXAVbjeRjPI9PYUBJgTSvAKiIyqIY8wLpp0yauu+46MpkMRxxxBGefffYuz3nppZf4wQ9+gLWWU089lZNPPnnQx3XTTTfxqU99irVr1/Y4DoB99tknv23WrFlALqu1I1O1q45rdgRjpffWbH2Z3P3d9sB3N6UYWpxcBmsH66AMVpFCYUjQQ61ogKb4HLJZZbCKiIiIDIXxQYTQRIu2xRMJcFzcsK3k+P1TY0u25YUhWbfgs5ExeL0sEfBv7wtpjWbbC8DmpDLZXp0rIiK9M6QB1o0bN3L11VfT3NzM/Pnz+cIXvlCUDVrOSy+9xHXXXUc2m+Xoo4/m4x//+JCMzfM80uk0//d//1d2/6pVq/IB1ne961357ZMnT2bKlCkEQcBTTz1Vcl4Yhrz44osAzJw5cwhGPrJ5tvjfh+kyiei4C3vr11bg2IA1NbBmAjwzNVCAVUa9ZMSSjFjqEklsGPZYIgDAGpcWZS+IiIiIDIkGxydwPJJRaImCg0M0GsG4LgaLG6aLjh/j99CjJAzxx42HRCL36EUGa4fA2NISASkFWEVEBtOQBVgbGhr4+te/TmNjIwcccACXXXYZkUikx3M2btzItddeSyqVYuHChfzbv/3bLgOy/fX+978f13V54okn+OMf/0hYsEx25cqVXH/99VhrmTFjBkcffXTRuSeccAIAd911F21txXceH330UXbu3InruixZsmRIxj6SeV1jpAUZrAc1uCydcwxL5xyDiVdgCjJYXRwyKtQu0ikMeywR0KFNAVYRERGRIRHF0FARIXQgcCBsayPiOeC2NyLtksXqO7FuS9jZMMQ//AhYvDj3MIaI27vPygEWp0swt00ZrCIig6r8+utBcN9999HY2AjA5s2bOe+887o99qtf/Srz58/n97//Pel07gf/yy+/zLJly7o953vf+x6TJk0CYPv27Vx00UUAXHDBBRx33HG7HN++++7Lueeey3//939z2223cffddzNp0iSam5vz9VUPOuggLr74Yly3OEhx0kkncf/997N9+3a+8Y1vcP755zNlyhSefPJJfv7znwNw9NFHM27cuF2OQ4pFwy4ZrF6Ei/9zCQA3XFvH0o7tjovbpTmPn1WgSCTPhgTOru+hpfR9IyIiIjIkItYhNAVJRtYScx0Ic3O0rnVYfacCshmIFpd5stbmmlwVrfbbdQbraefnGjQnM0kOe6s4wKomVyIig2vIAqwbNmzIfx0EAUHQfXZhx126/pzT8XU2m7sDF/ahYctJJ53E9OnTueeee1i1ahWvv/461dXVHHbYYRx77LEcfvjhOGUCFGPGjOGLX/wi3/nOd1i7di2XXnpp0f7Jkydzzjnn9Hoc0qkowBpaTCzG8mXLc89vLWiO5ro4tnhSkFUGq4xyN96f+7MtGsCykMAU//xybIjtsq0tqyZXIiIiIkPBtYbQFH7ktkQ8BxPktp20ro3HCqrKBU4cm8lgugRYsRZrwS8sotqHGqwAH3o5xZ1Lcl+HBtpSmR6PFxGRvhmyAOsVV1zR53N+8IMf9Ou1Jk+ezJ133tmvc+fOnctll13W5/OWLl3KNddcw2233Zav1WqM4V3vehfnnnsuY8aM6dd4RrtYWBz8MZGCycXy5Z1fp1IYigOqga+7sCIdbFBag/WAhs34TgTDVNz2fcpeEBERERl8NgxxgKpsBCds7y9lIeo6uQ69QNzvzGBduA3mvu1hZ86ABYuo/STUbakDoP4/thMChS0nTC9qsC6ZugTIXSeSbS3a19aWKnOGiIj015AFWEeDGTNmcNVVV1FfX099fT1Tpkyhurp6Tw9rr3bwuNnAuvxzE42WPc64blENVgDfVyaeSJ4tzmBtiwCEuLY4WyGtDFYRERGRwde+GrMx5jGpDRwLYIm6DsbmbnTHs8U1WFsjFdDegyT59OM8+L+5G+F1t01m2leuLb5+H2qwAkTSxQHWbCqNH4R4vWyUJSIiPVOAdRDU1NRQU1Ozp4cxIrz59mv5ry222wArjoPbJYNVJQJECoQhfpcMVtcGeL5PIgNU5mpytakGq4iIiMigs+0B1tDprMFqLEQ9B0IXMFSUC7B2o6g8ALQHWHsfHHWzBRmrxmADn5QfklCAVURkUCjAKsNKSZOrSKz8gY5DdMIEKJiTZFUiQEa5uTtyf4bGYm1IYIoDrC2RkBgZAgMde1LK/BYREREZfKHPslVRXpkUxQ3BGog6USKuwXi5j+GVXZbtp70oGdelAvIZsJArL1Dc4Io+12D1sikMIZb2gKof0JYNSMQUEhARGQz6aSrDSiw07KjMfT0h5VDRXYkAY4AAb8w4ABZPWawSASIAxuSmzV/6EkFkQtEuNwyIBcUdZNv0fSMiIiIy6GwQsCWaIeN4WMBgAEvMdTAml0RS0SXACtAaq6Akj9UYsr/7A+z7ztzzxYuJeG77Z6Lu5ZsFAyv/uxo3zOA78fbx+aSyWgEoIjJYFGCVYeW+Kc18blsTxnGY1hwhst/0bo89/aBT+EdL55IbP9AEQUav0DFk9p1IGIvgtqWJQEkGqxsGxPziGqzKYBUREREZAmVKBABEPAdDLsDq2gBjAwLj0tG/Ku3lEkwqspYLP5jbVm1a+PGW4nld1Ct+vivWhnhBOh9gJfBJqRa/iMigUYBVhhXfyXXctGGIkwanvU5kOW6Xou4KsMpolqmOE7ZX1AgqYmS2bSY8YGrRMY4NiPlp5u6AxNzFQC7Aaq3dZQaEiIiIiPRBEBAaQ5i/4W0xkGty5cVwjMFg8cIMFOSspttLpMWsw5xUFREMb0WzZJzij+7RSB8DrGHY/lrtz4NciQARERkcqmgte62uRd2zge7AyuiVHhMv3mAg6PIj3rUhMT9VtC0IQjL63hEREREZVDYISLkF/STaU1QrIi5EOsugHb45i2vJt7BKtQdYD0uNY14mwZxMFce0jCdbGGA1ps8ZrFiLG+ZKRbV6lmc3P8O//emiPr4rERHpjjJYZdg56O3cnxV+z3dU3S4BVtVgFYFfL8z9+f71GQInN/GuyAIWnDAg3iXAaq0lmQ6I9XWSLiIiIiLdC3zSXvENcGMtMc/BGEPUjQLpkvJNjx54OPs2rWVyECNb8HGnMIM1d37fcqWstUCawMmVljIWsOX7XYiISN8pg1X2Wp7TNYNVS1xkdLKBX3a777i8WgNtEcDkMlhdGxL3CxpdWUsyXf58EREREekfGwakCgKsFktlJpUvy7QmkQusVqVbSs5dMX5/HNv53DGGjFuYweoQ8/r2Ud4xDm5BiQDHgrWRHs4QEZG+UAarDDsdy2Oc0PZ4XNRzgM6s1UDLnGWUCpPJkm0ZYwmMk/9+Cn2fiQ1tTGiDCa1tNHYcaO2uO8jW1nZ+vXx598eJiIiICAA2myXjFQQwLcSznTe5/zm2DRtGGZNuBqAqA14IZDLsaG2AGoM1loxTBRi2Juth3IzcycaUlEvblYXJSiJBGmOhKmMxfhI2bBnQexQRkU4KsMpeq6REgAKsMkrZ1lyANRaAsWAN4Jh8iQBDrrFBfGs9bpjECwsyVq3F38XNDIDaxXW5L26rZfkyBVlFREREehT4ZJ2CAKvrEp3W2YC0IRLSUL+VA/wEsCC/3QJt7XVYt1cdxLbqdwDguJDouKluTJ8zWI2FSFBcKso4YwlCi+uo2amIyECpRIAMO7b9Ee7iF32kpERA2F5bSGR0CdOpstt9p7CuqsUNAowFNyzIWLVWNydEREREBpn1fXy3eAl+tGAOtmTqErAwtqGesW2v5rc/Mi/GG+Mr8I3L9up55S/uOO2r+fpg4ULavHpaO4YUBJispaEt27friIhIWcpglb2W57ms3Loy//yd++xHEFo8V3dgZXSx3QRYA1MQYLXkMleNJdIlgzW7qwzWujr+eWQDAG0bHx/gaEVERERGga4ZrFgiYXFZJosFa6lObQTmAOBi8KNxskEN1ji5zJOu+tHkygCuTeOFbUBF+zZDY1uWCVVqdiUiMlAKsMqws3pi7s+DG1yqezjOdR2SmYLakzYkG1jUDF1GG5vNQCZD1nHZVj0H3/FIuevzJQIAsg64QQCuxStoimWVwSoiIiIy6GzWJ1vQmMpaiIWljUWNtbhhrjZrMgrxLGAdWmI13V7b9CPA2iESJMHkAqwO0JxSBquIyGBQiQAZVpZMXYJp/686mujx2KjX5f5AaPFDBYpk9LHp3KT88RmHsWXMO9ieOIhHZ59IaIqzuTtqrxaXCAh7zmCtrYVksqiTrYiIiIjsQuDjO8UlAgpXEeVr2luLE2ZKTm+LjG//M/cICj+5m76XCDAYAmOxNrfyycdigLRutIuIDAoFWGXYcR0X19l1GmrXJlfYsFfNekRGnGyGwBg2jduHZDSX/dDm5pojLN4CiUwu08ENArBdSgSElmwvJtbJCLRGIOiytE1ERERESlnfJ+NGmFtvqPRhfHxc2QxWrMUhKG5CCkxv2odUd+tNjSHaj7JooTE4tjNj1bGGjK8Aq4jIYFCAVfZarudibGewx/ayG7rISGOtJe1E2/MQujsol7latsmVvm9EREREBpUNfHynM0JqoKQGK8C6cZbxKYj5pVms3XIM0X7URfONxbE+bV4uK3ZHhSWjDFYRkUGhGqyy93JyAVbb0chHtSRltLrictJebgla96FSm8uMsJHiDAl934iIiIgMPj9L1s3Nz9oihtjJ7yV63JKiQwwmP3mrzGTYUVmZ+9oHp6fpmXGI9bFEAHQEWDszWA0og1VEZJAowCrDyvJly+HW2t4d7Lo4BOSnBOEuakmKjGCbxsSKa3OV8er4gFjMoyWiDFYRERGRoWR9n2xhDVantG6q67g47TXz436619c2xhDpR4kA31gMXQKsutEuIjIoFGCVvZZxXRxbnImnO7AyWqW9WI/7nTDEwWKBCIWlNUKyQS8CrBYe/h+HkBAerYXlywc4YhEREZERzPfx3YKP245DtGsPCXKlngCiQWmJgEQGmqOllzaOKXutXcmagEjYGWCtDF19fhIRGSSqwSp7L8fBdAmw9qZZj8hIYy+/nIxbZvbdbvFWw/6JKXiOh+u4eIUT8l1939TVYYOAU3eOZ9zEKYyrmVyYNy4iIiIiZQRdMljNLgKs8WxnBmvHre9FW3J/hk5xtmrMdTCmHxmsWIzNUuFDhQ+zW6KkW9v6fB0RESmlAKvstYzrFdUQstaS6U0mnshIEwa7zGD1bC4oaq3tc5MrvzLGrFQcAMd1SbulDRpEREREpFM2my3eUKZEwGnnJzj/rDhbEoYx6WSP16vKAEEAQUCl1/fgKkAEt+jzE0Dbjrf7dS0RESmmAKsMP8uXdz564jjFJQLCUBmsMjqFtscMVgAvbP/esJQ0uerp+6b2Q42snV5FpOCQTa1vDmS0IiIiIiNeJtvlhnQ3GawpJ3eje0b9G4xJNRXv7CaOGu9ngDXpBvkAa5sHrRH4v5f/lM+iBaC2tvMhIiK9pgCr7L3cLndgrVWRdhmVbHsGa1t7ma+wzJzbswFLkwne0VLJtIqagpMtfmCLJ9ZFF7cYp/SCNlAWq4iIiEh30n7Auplj8w/HcUsaU+388k5e+ff1xL0YLhlOWLecmtbXaIlCUxzavBCH1s4TrAVrqYy4/RpTVeDhhsVNrrxQja5ERAaDmlzJXqu0yVVIVkXaZTSytqhEwKKtuaYIABgDrotrO743LK4tLBEQYrEE1uJ1V8vLGCzwpwM9XOvj2DT/3tKMO2bcELwZERERkb1f1xVCnueWrZtqqhK0urljPRswrXEFR76xju1Vk5ia3MGaCdNpis8pOqci0r88qQ2xNPtlOhNjLZBtbaItGxLz+he0FRGRHGWwyt7L6RJgDZXBKqPUl760yxIBkfYSAcZCpKAGa0fmqt9N/WJjoTVezRvj38MrU0/jpakfpjG+L2GyeZAGLyIiIjLyZLokfnStv9rBOA4PTEqyzcti2yOfY9JNzK5fx7hUI06YLjmnoptr7Yo18MD4HTgFWazR0JBsaunX9UREpJMCrLLXMl1KBFirGqwyemW8XIA16JoYYS0EQWdjK2vzDa86ntseGl3FrMPqKfOIhGNyG4xh87hDSDU1lT1eREREZLSz1hJ0mVtFesgQ3RoPSNXvIN3UWLIvYVtwHTeXdWoMGENltB8LUZcvh6VLaI5BJGgr2pVsbe3mJBER6S0FWGXvVVIiwJLpJgtPZCQLDaTdzhIB62qgLQKBQ75EgGdDWLIEFizkhpmbeWLTEzyx6YncCd01uqqtZWwYYUflhKLNgePx+s620uNFREREBMKAOx+7mebWBppbGwBwyzS46o0K/21i/s7880iQZVoi0q9rLV+2nLQN8MLieVxLW7abM7pQAywRkW4pwCp7LVNSIiAsWYojMhqkvBih6ebHuetCIkEsyH2vGMBgMYV1WMOQbJmbExbLu9ITCZwqFm8lV6ir3esNpcvVRERERAQIQ0JTnLHqubuucXrtMSZfJqCDwTKh8XEWv/kiB25fy/vXPEZldAD1Ug2YoLgkQHOb5nUiIgOlJley9yqTwaoSATLq1NbS1nUmXsa4bHGmwtLNmVzd1vqVMG8hflj6vZN1QtJegliZ4G1z2i/ZJiIiIiJggwBL7wOsyUySVs/yRiTF//fiGCIhYCEMfNJOFDfI8I6tazAYYtZg3IF9jDe2uCRASyrTzZEiItJbCrDK3ssprsGKDdXkSkaltmjFLo+pzqaAzq6xXujnG2PZ0BZnsLYv+8q8uYHUvlMIKJXMKMAqIiIio0/tbZ3L45cvW17+oDAgNG6+Nn4yk8TrRWOqbzwYQLwFKqty5c+am8k4FezfbKmvyC0/nepHwRnYQlQbpIqepzSvExEZMAVYZa9lHKcog9W2Z7BaazFm1xl9IiNFxuuhDleQC49Gw4LvFUNn0yuAMCybwRpGXAInmsui6CKVCfDDEG+AE3wRERGRkcYGAbbLCiC3h6zToGBelmlqYvrGJFiLCS1Zp7SMk/H6/zHedVwsxSUBsgqwiogMmD4Zy97L9TBdSgTkgqxqdCWjS8YtH2Bt88jVYF28uKAGqyEwFjf0eXW8ZWUiye9evAO/XA1WA4HTTfA2DElllTEuIiIiUqK9BqtrwbWQiCbwvO5LBLhO8T4ThJjQEjqGjLF0FMK37X+aaKzrJfo2PJslMOQeYUDWV4BVRGSgFGCVvZZxHBy6dLwMrcoEyKhisWQjURIZSGS7z9yO/v5OWL4cli/H75LBaiz4YXGANTeBN4SmOMCainuk4h5/3/g4qWy54gEiIiIio1wQYAuaXFnAdfu+ws4JLRknVx4g6+YeMPAA6//vpUw++AuQ6c2crrYW6upyDxERKaESAbL3cl0c22UyYEMyfggDm3OI7FUyTueP8u6m7pGCrImssXgF3zsO4Je7MWHas2OzZXZZSPm6mSEiIiLSlW2vwZp/buhTWaU1E9q/MIZya/NMLN7vsSWiCVy/uKlVxg9UZk1EZICUwSp7L9fFYIvqsGKVwSqjiwWyBSUC3HIhVmOIRzqDsL6xRRmsTpkM1g6FJQIKr+xYlMEqIiIiUo7vF2WwNmWa8Zz+BS9njZ1ess1Eo/0eGoBHkC8RYLGEQUDQzVxQRER6RwFW2WuZ9lpFHXVYV25dya+f/zUf/d3Ze3JYIrtdtkvThGS0fcLcMY83BrdgUp8rEdB5Y6J8iYCc0JRf6OBYdDNDREREpAzrZzG286N2YOh3gDXqlwl89tTgtBecoEvN1SDsnAvW1VE7+/Hc47baAb2OiMhoohIBsvdycwFWx2YJiDN7Y5Lpa1O8c/2r8Ok9PDaR3cRi2RF3SEbJpZi6LoTFk2ZjTNGSL9+xTGjxcUPABjgWspd9Gba/WnL9wgzWuTtg5X65r1vSSbIqESAiIiJSwmazhKYwwGqLbnb3xcW1/x9fv/18CosFDHQp/5Xv96gpmMbZMMAPLbW31XJDIknSDUgE3Tfl6qowELt82fIBjU1EZG+lAKvstYzjEBpwrM+CtwIILKQzZNNlCkaKjFDWQODsagJcPAkPCkoEWGupb9uB33GN9sYFNuLC1DElTa46OEAqq46zIiIiIl3ZTBpbsAooMPQYYE1EExgvCWEA2PwqpFTcJXrwInzPtDcghVhTasDjy5ouc7gwLF+Pv1BdHSSTA35tEZGRSiUCZK8Wmi41WA347sCWzIjsbQKziwBrlywH31DU5Mq14Jsyvw4yGWw3AVaAbEY3M0RERGT0ueHaOm64tg5qu1lCXyaDdVclAk47P8FJ57o0d2nW603dl39WtxAGAdl0imjzwAKsS6Yu4cCJB2JsYQqrJbuLGqy1ZyRZMSlgxSTV4BcRKUcBVtmr+e0ZrJ2MAqwyqthXVhHsqittSYDV4hXU3nKs6cxg7biuYyC0LNkSwdA1BzYnoyZXIiIiIiVsNkNYcAM8oOcM1kInn+uRjOZq6ieiCYzj8Pi4JE07ttHSuBNnEJpRhQZMwc12rC3KYA2MJekG1G2p6/U167bUUbelTnVbRWTUUoBV9mqBsTi2OItOAVYZTWxbW0kGa8RvLno+u6I4EJo1Fq+9TqsBJlSMJ9s1g7W1ldZInGzH95MtncxnVCJAREREpETY1IAtmJ/5jsXb1Q1xcgHVRDSB53iMi49j6dQlndc0uceKxMCX6ecCrD6m/T+sLWl4KiIifbNbA6xhGBIEyniSwRNSnMFqDJ0BIZHRwJiSGqzVqXVUp3NB1miQ5eAuAVbfgXhB9qmxcN2U16id/TgrKhqhsREbBrxVPSl/jMWWZMJmfAVYRURERLoKvnE1tuDmdYsb9jqDdcnUJSSiiaJtiWgCx4IzCDHQ5cuWc8uHb8UlxLb/99C6PyvAKiIyQEPe5Grt2rXcfffdvPLKKyTbi2JPmTKF448/ntNOO41oNFpyTltbG3fddRdPPfUUO3bsYPz48Rx//PF8+MMfJhIZ/ODZ+vXr+cMf/sCqVatIp9PMnDmTM844g6VLl/Z43lNPPcUf//hHNmzYgOM4LFy4kLPPPptp06YN+hilPN9YgqCNV8db5tRD1gHfK/03JTJSWccU1fhKRBN4ZDhh3cO0Rscxu9UnEStequUXZLACeCGEOAQ2wFqLXxGjbco4No+dUnBW6YeCrEoEiIiIiJSwWBZv8fjHvrnnGcf2GGBdUpCpunzZcljW/bWDcBDmX66LZyDT/tRg8IOeA6xJNyBAQVgRke4MaYD10Ucf5aabbiIMQ2KxGAceeCBNTU1s3bqVO++8k5UrV3LllVcWBU2TySRf+9rX2LhxIwDGGLZv387vf/97XnnlFb761a/iurvqmN17K1as4Nvf/nY+s9YYw5o1a7j22mu54IILOPHEE8ue99vf/pa77ror/9wYwzPPPMOLL77IVVddxcyZMwdtjNK9wJAvEfBqDWzyDE37OZy3h8clsrtYx+A7xT/KjQ2Y1hxiqCdmHeiyJC1XIqBzcl6ZsUDu56rjebTsOwGL5a3qyYWvxJTmLcC++S0ZXwFWERERkULWWiwU1cgPDLtsctWTXAD20YEOLc84Lg4Fq5kAPwy54do65mwPSGTAddyiwK+IiPRsyEoEbNiwgZ/+9KeEYcgpp5zCz372M6655hp+8IMfcO655wKwevVq7r333qLzvve977Fx40ai0SgXXnghv/rVr7jxxhtZtGgRL774Ivfdd9+gjXHLli3ccMMNBEHAzJkz+fa3v81vfvMbvvrVr1JZWckvfvELtm3bVnLe448/ng+uHnPMMdx8883cfvvtLFu2jLa2Nn784x/ja+nsbhEaW1QiIGYdHJTBKqOIMYQFE/jFUxazb2Jqrp5WR9ZplwDrG/FsUQara6GCXMvaSFUVty62vD42RpuX25bIwNjKCSw0hiAM8o+0MlhFREREivlZMBQ1EA2MHVCAddA5TlGTK8eyyxIBqydYWiPQqmpsIiJlDVmA9Ve/+hW+73PaaadxzjnnEI/HgVym5/ve9z4OPfRQAJ544on8Oc899xzPP/88ABdccAG1tbV4nseECRO4+OKLqaqq4ne/+x3Nzc2lL9gPv/71r0mn04wdO5bLL7+cGTNm4DgOixcv5lOf+hTpdJrbb7+96JxMJsOvfvUrABYvXswXvvAFxo8fTyQS4YMf/CAnnHACGzdu5OGHHx6UMUrP/PYM1rk7AAvVJsosO4GP/OT4PT00kd3COqakyVVhRkJuQ/GP+q3xgEgmU7QtQgSsZZKpxADN8eqi/a4NmZgu/tmrDFYRERGRYjabIcQU1WAN6DmDdfmy5flHd4wxGGNwnUFYzdklwJrLYM0FWFfXWFq9XEkAERHpvSEJsKZSKaLRKLNnz+bMM88se8ycOXMA2LlzZ37bgw8+CMABBxzAscceW3R8ZWUl7373u8lms6xYsWLAY2xsbOTpp58G4PTTT6e6ujiYcPTRRzN+/HhWrFhBpiAQ8dxzz7Fjxw4APvaxj2G6NH059dRTAfLXlqGVy2DNUpnt3GZMjLnbNSGQ0SF0ujS5MgaHsOgYU6ZrbdcAq4OHBYzNTa7/eOBYQgcCJ3fNcSedQPTTnyw6JwgC/KD4tURERERGM5vNEpjiuVdgeq7BursZx6UmXo3p+M+a/JwuUxnlsNQ4DspU4XST1bpiS13R8xuurSNoaiRoaqSuyz4RkdFiSAKs8XicSy+9lG9961v5zNWu6uvrAYjFYvltr7zyCgBHHnlk2XM6mk4NRoB19erV2PZAwhFHHFGy33VdFi1aRDqdZtWqVfntHV9PmTKFWbNmlZy33377MWnSJFatWkUqlRrwOKVngYGPPd9WvM2Jkch0c4LICBO6LragAZWJRHFslxsMpvRHfUh7iYD2m0QODuDQ0bsgFRlTdPz4qKEi4ua7zVoshJaUrwCriIiISAebzeIbB9caQgO+A1MSU4dVgBXX5d0zjsd1XFzHxQGyQYhxHBLjJjAzW8GiVDUHvd27pJVkJpmrPWvVBEtERq8hKxHQkzAMefbZZwFYsGABAE1NTbS2tgIwd+7csudNnz4dgDfffHPAY9iyZQsAEyZMYOLEiT2+3ubNm0vO626MkMvADYKArVu3Dnic0rNk0EbUTwO5OpKQy7hb3RLr4SyRkSPrFRfCMpEopmsGa6z0+yGfwWotJJO4GDC5MgEAKW9s0fHj4y5Rz8Ng89kO2JCU6rCKiIiIdApDbp/yNslIbk7ltH9G8cqsKNpjHAfPFAdD/SDEi8UouG/Pwdt6N8+rCl0+vn0y526bwtyt6kUiIqOTt+tDBt8jjzySz2D94Ac/CFBUV3WfffYpe14ikcB13bKNp/qq4/W6ey2AMWNyGVzbt2/Pb0smk7s8b+zYsfnzOoK0vdFReqAn48aNw3UHoe7OCBECsfYAa6Hm2P4DvnbtbbX5r3uqhySyJwVdJusmGsXYrgHWipLztlUWT34dDJjcrwQLpLtksNbEPZyMixtmCZxcwNaGCrCKiIjI6HP+cY0AJILHKfmUEAaExs2vCuoIYw6nDFbjuHhAIprIb/P9ANeLENosW6vaj2vciLW2pCxeIWst8eoxjA1yn1GPbxpDmErhdLOSVURkpNrtAdadO3fy61//GoATTjiBmTNnApDNdhbRTCQSZc+FXC3W5uZmstkskUj/Wxh2vF5VVVW3x3SMo7BObMd5PY2x45qF5/XGhRdeuMtjbrzxRiZMmNCn645kXiSGQ+lSFIPaW8rokIx2/hi3WEw01l6Dtf37IgRTZoLrBNmi564FjAuOQ8atJDTFvx5qqqIQejg2zTu2RHMb//RHUkctGMy3IyIiIrJXs2FY1ODKYjEDrMG6fNlyVlw+iJ9vHAe3awZrN81LbaoNU1HZ/bX8LI7XOW/0rMHftJ7ogfMHZagiInuL3RpgDcOQH/3oRySTSSZNmsSyZcvy+5yCLKxoNNrtNbz2H96ZTGZAAdaO14uVWTpb7rW6ntfbMcrQCgz83xyofe1pXpj8rvx268R3ebd1V264tq7zybJuDxPZowKv+Me4icV4o3FD0W0Hp7L4htCSqUvw4qswNvcBIAwDXAwGD2sg4xbfeIoEWariUcJWlyhh0feVMlhFRERkNHl84+MsMD3UGrUhYTboLMZnAAahZn0PCT595rh4WJKZZH6Tf+21hG2tEI8QCeHsF8Aai/3A+2H5Y91eKmxrLawqQMZYwmTT4I1VRGQvsVsDrHfccQcvvPACkUiEiy66qCh7tCNg6bpuUbC1q4596XS6x+zTXel4Pc/r/q+gI4hQGCjty3npdOnS9Z7ceOONuzxm3LhxfbrmSLfP5FmYjeuoaW0o2u4Zl6wfEI3skSoYIrtN4BbfaHJjcd6Mpen48e5kfSL7lZYqsdbi2CDX5dZaHAyuyV3Ld2O0RjqXtFVmUzixGKHr4trin2tqciUiIiKjVdItc6M5CAljFawfY2iLWKLV4xmUAOsgMq5bWoPVuDjRKCnHY2v1ITx04DgOaNjA/FRITwXqbCrXcDjb/hHeGkuoZs8iMgrttujTk08+yd133w3AZz7zGQ488MCi/R1L7oMgoKGhodtAYkcN1IHqeL2OWrDltLS0ABR1Q+wI6vbmvL7S0v++e2Osy0xjiQWdQfCOuUI6nVWAVUY0G/hk3eJ/417E496JDfywPooBqupTmGhxpv7yZcvJ3nYcbujTGM8FVSNZg0sMyOI7xcdX+CmIRDGOi2s7v9csymAVERGR0SUIe5772DCgLTAEWCqy8M79juDUxUt2z+B6y3Fwuyz0yzoOE1otfz94KS2xfWmNwqrJ7+CQt99gdjeXqb1oPN//o0+0urpou20ZnM/sIiJ7k90SfVqzZg0/+clPADj99NOpra0tOSaRSBCLxUin02zbtq1sgDWVSuWzQuMDLJo9ceJEgB4bZjU0NJS8Vm/Oa2xsHJQxyq61Rg2PV+zk3LYx7Z3THVwMiWiCtnSG6kRpcx+RkcJmsyVNrjzPI+NYvMbcjR4nKF8mw2A48o00j82oIHDAN4ZspALIkvGqCAwko7lVbXEb5IK0nodji2u3ZpTBKiIiIqPMqzW5P02ZXhCEIYHTWU7OGEM80v0Kzd66+D+X5L8ecPtdx8HrMvaMY/CNy+ax+xVtXzNmSrcBVshlwwJk3QQt0X2pyNYTtvUv4UhEZG828J/0u7Bp0yauu+46MpkMRxxxBGeffXa3x86aNQuA9evXl92/du1aACoqKqis7KHQdi90vNbWrVu7zTjteL2OoGpvxtjdeTJ0tnhpYpvfxgmLAz/pdLabM0RGBpvNEJjiRVteL7O2DRDzO5f7O4Dr5H6utkSLf3aNSzXjjqvBuB7GFk/Ge6hAJiIiIjLiREPD0pYqahvHMiVTpidIGOKYKC4GFwPGIe71tMh+9zPG4HW5SZ91HNoipf1JNlXloskHtVUwdtJUxk3aB6+gj4njumyvmsiWsSfTXPEOto89jie6X+wpIjJiDWmAdePGjVx99dU0Nzczf/58vvCFL/TYdGjRokUAPPHEE2X3P//880BnkHMgJk+ezJQpUwiCgKeeeqpkfxiGvPjiiwDMnDkzv33hwoUYY3jttdd46623Ss7bvHkzO3bsKDlPhpABrMULi5uKpTIKsMoIl8kQOAUTdgte1wm8282E3kK8KMBq+FRdDGs92iLjig6dktyBU1nVfq3ikGpoFWKVXRg/vvMhIiKylzt1x3iOah7D/LZKTt85IdcYqoANA7JuwUpGY4gNQgbr8mXL84/B4HWpEZB1XNJe6QrMjOOSamriI1vGEbWGiIVIRSU2k5tH/vwQWDNxbv74AMurbQ5tKTV8FpHRZcgCrA0NDXz961+nsbGRAw44gMsuu4xIpMwdvgLHHXccxhhWr17Ns88+W7SvqamJhx9+GIBDDjlkUMZ4wgknAHDXXXfR1tZWtO/RRx9l586duK7LkiVL8tsnTpzIggULsNbyq1/9quSaHXVm999/fyZNmjQo45TuFU4wjtqYIZGBRJALKKUy/p4alshuEaZT+EUBVluSjdAdgyEWFN+EyEYr2J6YwOKtMDYNrgUvDDngjTdyB7guhhAntDihxTY2oPiqiIiIjBZha5L9053Zm541ZDd2WdkYhmTcgmbMxpCIDb++EJ5bPGf0m5pJe9GS46yB5tc34NjOgKwxEL73ZEgm8axHMjYZgAXbYNFWaHrmCZqbVSZAREaXIftJf9999+VrkW7evJnzzjuv22O/+tWvMn/+fCZNmsTRRx/N448/zg9+8APOO+883vWud/HGG29w8803k0wmqaqqKqnh+vLLL3PNNdcUXas3TjrpJO6//362b9/ON77xDc4//3ymTJnCk08+yc9//nMAjj766JJ6sKeffjovvPACzzzzDDfeeCP/8i//guu63H333fz1r38F4JRTTunVGGRw5JY7Z0hGIXQCSDUqg1VGPJtO4djOybG1FtfJTX474p7NbkB1mXMNuQzWZPs8usKHcOwEdnT5tkm8/QYVLblOsMYt/ZVhVSRARERERomP3PZBpnfZFjY3dtkQkHELytkZw5hYz4lGe0JJiQDPI1UmwAqQ3PxGyTZrgCAgiEwlDEtv8Le0pUu2iYiMZEMWYN2wYUP+6yAICILuuy3aghSoc889lzfeeIMNGzbwwx/+kB/+8If5fcYYzj//fBKJRMn52Wy25Fq7MmbMGL74xS/yne98h7Vr13LppZcW7Z88eTLnnHNOyXmLFi3izDPP5Pe//z3Lly9n+fLiZRqHHnooxx9/fK/HIQNXmYWqdPEv8bQyWGWEs10zWLF4Tq7J2wtTGgCoDl2WdnN+YYkAgLQb45XJnW0MbBCQaNreeYDrFtdgNYZQ8VUREREZJTa8tYrp5IKlgYnh2DRhsrn4oNCWlAioig2vGqxQWiIgdBww5QOsLdvfLn8Ra3Hc8iWAWtUPQ0RGmSELsF5xxRX9Oi+RSHD11Vdz55138tBDD5FuD5rtu+++LFu2rGi5fod3vOMd3Hnnnf16vaVLl3LNNddw22238dJLLwG5QO673vUuzj33XMaMGVP2vLPOOosDDjiA3/zmN/larLFYjA984AN85CMfwenlMl0ZPNEuwaJUtvugfm/M2Zgc0PkiQ82m00U1WAszWHfFYIhnU0XbGirGUemDaw2hyUVO4+nO5V25LrFh8RhUI0BERERGiXhjC5FgHG+NPYa2yGQiQTPJ1hQFBQHI+j6h05mxaoyhIjIMA6xlPq+2eaVNrizQ6odlt0etg++VBlgDG9CSVrKLiIwuw68YDBCPxznnnHM466yzePPNN6moqGCfffYZstebMWMGV111FfX19dTX1zNlyhSqq8stqi12xBFH8K53vYu33nqLVCrFfvvtRzRa/q6fDJ3QMYAlnu2awTqwAKvIsBf4BMYhaJ8fB4EtqafVreXLaTuhu9zWdtZSmS640VBQIiA00OgFqsEqIiIio0ZV6NIUn0VbJFdzNOtWs7Ih5OSCY9KXfQXmH5x7YgwM1wBrmTljKhLHLY2lkrLl5peW6tAjFSmfkKQMVhEZbYZlgLVDPB5n1qxZu+31ampqqKmp6dM5xhimTZs2RCOS3nhtxliSsUacsLhTZcof4F3THspaiAwH1veLSgRYwDWGJVOXYMxjuzz/L0fX9PjP3FqYsvMNaM/LMK6HwVKRhRcnQ8ZYbv7jv7FxwV0DfCciIiIiw19V6NIaW1S0bXXSFAVY29yCeqvWYhyHmDf8VjdGvNKgb8qLkciUHvvHZ//ARGxustm+WMoC+/pxGiO5eWIik9vVce+9VRmsIjLKDOsAq0hvvTDNpSlefJc0lR3YL/UPnt3ZvudvA7qSyNCwgU/YtQZrez2tjqX7QdhDBNX0nFkw4+1X8cKC7yPXZWJF5zIwU/C/IiIiIiPd5EyEVKxz7mOMwQY+2axPJJL7aJ3p0hQ06jkYM/zmS47j5O6mF4wtVVAioHCRku/kVmnePzf3PBKm+ewOy/RgHCvpDB6/WtN59jvUD0NERpnhdytNpI+WTF1CIpooqn0EkM6WWd/SB4WTitrbagd0LZEh0SWDFUuva7ACWCfA2O4nv2NbdxY9z9Vg7eRgUIBVRERERoMwlaLa5potV7Tfo7bW8sSmJ6hvbs0fl+4yXxqO2asAjuviUHwjPhWNgzEEDqyryT1WJpL4ZZpfZZyAwM0FZFs9CAqmhAZDxtdqQBEZXYbnT3uRfogGXWqw+qEa8MiIZgOf0BSWCLB4fQiw+q7BDdPd7t9/Zxtz37aQbK/D2l4ioCWWm3iHxmAUYBUREZFRwKbb8J3Ksvte39ac/zrrFAdYyy3FHxZcB2O7BFi90kBqEAaETkd2biUN8f3wnRjWgO+UNsXqoACriIw2KhEgI0bELy4YZMOAtB8S70dR+RXzxmOObL9Ox8bagizW5cv7N0iRwRT4BE7Bj/E+ZrD6Djg2Q0eN1TUTYO4OMO3/6mNdGscZ1+W0g07hga0rAb89f1UBVhERERn5bDqF78TzzzsyNk0YsL25Lb8943QpEeAO0wCr45YEWP32sZsuOSqBidAaqeG1icdjMWwP0rSt+Rv+lETZS1ssGX9gqwlFRPY2ymCVEeHi/1zCHR/ch7AwuBRa0gP4xV4ROuzjx4iH+jaR4cn6PoFT+O/T4vWhxtcVJ/4XTth9HdZYNl0UPjUVldz3yr0E7ZNxA1QE+v4QERGRkc+m0wROBXPq6ayQ1P5nQ0vnTens9h1F50X7keyxO5gyJQK6ExiPtxMHYdvfcODGeWXs/vnarJC7SV9YYy3rB1pNKCKjijJYZcQInQDPgO1YMm3DfgdYW4M2TmqbjGcNGRNy8refhzfbr7VkyeAMWGSgAp+gS1aE5/Y+4Gm8CF7Y0u3+WDpV9NyJV/BGehv7233z26qC4fmhQURERGQw2VQboRMp3Y4lmcoSWotz4omkw+K5UXS4lghwSksEdCdwIjTH9+l8juX5qQfjuy92e461IdnAEvW02klERgcFWGWvt3xZbrl+2zN/49N3/YqgPcBqw5BUP2v/xCqq8KxhaxWAg+/q7qsMPzYI8CMx3PbYfxj2rUQAXoTqzGZa4rMobusGroWKVFvJKQ0Rn/0LnlcF+jUiIiIiI1+YaiU0pfMegyEMfFozAQkg60VYvKVjpxm2GawlJQKCANozTl0MbV7H3NBSHXpAQWNUY3Ad06UGq8W1hrBjNVVoyQQh0WHa5EtEZLDpp52MHI6LawvqsIYh6Wz/Mlhj8Yri5xXlC9qL7FFhSFDYSMF2NrkKHUPoGNri3U/qjRch7r+df94WgdCBwIFImMINA+hScqDJC4rKBsRUQkNERERGAdvWii0TYAUgCEimcwHIjFec5RqNDM+b0cZxel0igCBg7o7cDfiOvBM3DPIB1jUT4KWJlsLKUTYMyQaqwyoio4c+GcuIYVwXNywIsFpLehC7V65IJDnsrEYOm/c4tbfV7voEkaEWBsUBVjozWE861+Wkc11OO7988wEA0z7hP/fZp/NZsB2ifmuugZXrQqLzGiGWRAbcMPeYUDFhkN6MiIiIyPAVtrYQmtISAZBrrptM+9jHHiMdaa9LanLzqFhlRdlz9rhdlAgInM5HORk3gu8WNv2yxc1PB1CuTURkb6QAq4wcnodjCxr22P43uYqWmWskHR/fWPzQp25LXf/GKDKIbBgSmOIf430tEQAwrWkzbpgu2jUmvQUTln7/WDq3mYL/FRERERnJbFsrVZkIbggHvZ3L5MwHFIOQlkyAdR0ybnHGaqxyeK6EM67X6xqsyYjNB1tDQ36FVMobw9wduQZXi7Z0KammDFYRGWWG53oFkX4wrotTUCKgvzVYbXtQKeuALYgdGccBNEmQYSQICYzLugm5f6ivT9+X8/oQYDVehKgbJRakmbnj7zw/7SggythUAwHrWLdv7gPB0qlL8udYY7HAgm3gOR7VR713EN+QiIiIyPAUtrUSOKUfnxdtsTz0j9/ym2eu5J5pY/nb9Aj7NgNYEiFEh3EGa29KBBgMtqBWf2EY1XdjrG1fzJR0DInCnTZXg1VEZLRQgFVGDscrrsFqbb9qsNpM7hqRELCGxvgUNlZHGLOzHiht+iOyx4QBfkGJAEtnBmsimlvWv6QgONqV8SJkggxZx1KZrWf/+j9zwrpKxqSamJGOMbbMuZawM2c1CDp6IYh0L5nc0yMQEREZEBuG2FRrSYkAi2VsGpxMFtc3BJUxfCea32+sJTZMa7DmSgT4ZXclKsbSkGrIP6/M5kpDUWbeN3dH7s+xKZdn9wuxHYtkw5BMudWEtQWl1pYv79/YRUSGoWH6016k73I1WAtKBIQhqX6UCLCZVP7rbdWH0lixP8/tB5PGTefDTX9lcyTFW4GWRcueFwYBQUFmtTXgGsPyZct7VSfYRDo/JHzsBfj+EVnGpRo7Dyg76S2eWYeKsIqIiMhIF/hgLe/aFqfZzX0OCEznHMi1hripzm0vCLACxCPDsyqfcdzSEgFm159xXNtxI7/4BqoJLRG/lYyXu8lvrSUbaJ4oIqOHAqwycnQpEYDtuUTA+OvG57/e+eWdnae1Z7C2RCcQuPvnO2VuT0yGYByzM00saB4mkwXdAR61bBgSWostqIFqAc/t/STeRGPF1yz4OtKSoVeGybeCiIiIyFCxvg/XXUd27nvAseB5YDqzP10MuGPIOi6hya0u6ig1Fvfccpfc8xyHIEgThL0vqdbWfm/eZMqvTsnG0rRFxuaehCHZMvX8RURGquF5O02kH4zrFpUIsP1tcuXnsmDrK6cXbX5hMmweM44tCVjYHC93psjuE4YEXRpMWWPw+lKDNRorm4Hq+z6xA2aXPccSYmmPq7quMlhFRERk5AtyQchsQQOrfIMrC541uCbKjsqa4vOsJeYN04/crovpRQ1WaA8gt8t/dfTRMG4c6yZ7rJnsEvnUJ4ubptoQXxmsIjKKKINVRo72DNYgDFi0xdL01kNUP/53+Pk1fbqMbQ+wtkVyGa4Lt0FVe9w26+aW/lSoRIDsaWFAx5x18fZcZsThp30Mt31p1/Jlu85oNrHODNZtudVcTNmRJfLm27jBzrLnWCwtsdxrjI0l1PZNRERERjwb+ATGITCdwdLCxk8RHKYEcd4aM6XovEgqO2wDrMZxcbqWCCjc3x5KdR0XCgKxFgjDAFauhEQukzUcU00s4hbXdA3V5EpERhcFWGXEMK6HG2bzk53ABmRcj9BanF7UE+pgfR8LpL3q/Da3fW7QWHEQkaCZWLhtMIfeN+M7SxuwZMkeG4bsWbZMBiuOk29y1SuRaPv3RkF2gaHrVYtft0tNACWwioiIyIgXBGS6NM81GCL+DkKnM2t1W2Jy0TEVbW2YPnwO2a0cBxv6nXM7tzQ0YLEEYUDQTU2oxcncHXoz/13EPBenMBBrQ7LdBFhrF9flvrittldJASIie4PheTtNpD8cp7gGa7uS7pW1tTB+PAvXNDLn9TL1gwKflBfHGgcMvFoDYcF3ytvVh9IQm156nsjuVJDBmmcMntuHEgFlJvxbxrm8MAVIJMqeY7vkrFpFWEVERGSEs76P7xTUUm2fJ8WzbxcdV18xLv91mM1Ske1lTfs9wXUx+Ls+rheM4xKNuDi2uOGwSgSIyGiiDFYZObrUEeoIHflh336xW9+nNVJB2EOcqjk2m9DP4niR7g8aKslcULj24wE3PFUHwNKpS3b/OGTPCkozWB3H6VO2NuQaMORrqgLZdKrnE0zx95MWfomIiMiIUq6JbOCTjUTzmw25DM2o39D9dawl5g/fAKtxnB5LBHRYtMWyYRysq4HAlL9Bj+sSi3iYwuvZUCUCRGRUUQarjBildYRyv/z9vv5iD3zaIpXMrTf5qJMTQiIDrs0tB8q6Cd7e3jAo4+6r2n/1qf1Xn7opuiM8mtkwxO8SYHXdvv9If3RKhmgA0fZvnXSqrcfjZ42fxYbpY9kwfSwsXgxWWawiIiIystnAzzW4Mib3OOxwHJslEjR1f461xLPpbvfvca6LS4hp/69kd9g5J1y4DRZvNbjdTfkcpz2DtbAGa/clAkRERiIFWGXkcD1MLtrDuhp4tcZy25Qt/c5g7WrxlvYga/vyoG1vNwzGqEX6J/BLSgRE+hFgXV+ZJd2wk2xrK+uDeuItmXxTt3K61mAF6OO3mIiIiMjeJQjIOgUr1xyHpJsmEiQxPdxoHt4ZrG5xxmmBwIAXtpZsr8zmPgu5heUSyGXDxiJeUYDVWltaqg2onf04dRWN1FU0DvAdiIgMLwqwyohh3Nwv+rFpP7/mOZtN9znAiu/TEqkoG0iaUw8L3gpY8FZAa2vPmX4iQ6m0yZXtW4OrDgb8VIpMUyNveWkM3Sz9Knid4qdWGawiIiIyolnfJ1PQBMoYw1tVWbImIJFOkoxCMgpNcTrLjA33DFbH6bYG6zv3O5y4zZTNbC3LmPYSAQXXs5Zs0CWAW1sLQaAuqSIyIqkGq4wcTu5+gbE+mNwdZgPll6YkkzihpSJdetfW+ll8p7i26ty311NfMY6WaA2uyQVyWzODUxS+r4L22yLWkK/HSl3dHhmL7EFhWJTBaiz9C7AW+OOvoDprwHWhfI8rys2zNUUWERGRES0MyLjFGayJiZO5LbWN499oJjDVQPGcyA7zGqy5BsHlP88YzyubwVokWdAs2HWJRT2cgoBt8h9PkHz0/1jx/N0sXbVzMEYsIjKsKcAqI0d7BmthHdb3rwX/E8ug5e3OIvXtjAUntMx5PVm03WbSxV1CgXg2zb7Zrbw+via/LVkmODvUam+rLZq4ffqDuUmMaxv5R7mC/DJyhUFJkyu3jw2u+qdrONUSKgtBRERERjDr+2QLA6zGARPQ5oaMb6oHppU9bzhnsOb6V3STMOJ6RMKW/FMnBLA43Uz5jHGIRiMl1/PdiG7Ei8iooQCrjBjG9fjYcxluPDQg0xEfNQbf6VIJo64OfJ+6qblQUeAUTwRsNkvgePlm6W0mINaWwvVyG4L2AO6eyGB1Q8s70mMwFXGmNvm8FTbS6uz+QK/seTYMCWxBQNWC148arCVcFxIJWLKk/OuWlAjQKi8REREZ4YKAdLwyn9CB42BMltAx3HlwQ9EKHxcDWAywT+P2PTDYXnLd4iX9BYzrEQ2SnfM+0/E/3Uz6HIdYNNKl4TD83/Qs941rI3FbLcuXLcfW1TFvnyoOfruCrZEMTV0bCoiI7MVUg1VGjvZAqmuzRZszXbJRSSb58McjvLM1wbRstOQyNpshaC8DMHcHNLhZqrbsoHpnQ9FxrZndH9icVR8wJ1tFInA5IB3jyNZxu30MMkyEAV3/BfanRMCSqUvyX79aQ4/BVYAffuAHLJ6ymMVTFue3KcAqIiIiI8WKLXX5R+1ttayYN561n/gg6dDk6ocGAcY4XHfSNbiOi/G3Ap0NcTsmRpMa3mJcW/MefCe74DgYbPkgq+vg2jaMDVhXk6srGxiLMYZENEEimii5VjwWKbmWgwHTmdOVrYxycLaa8YHHQalK5tQrUURERg5lsMqIYRwHay1O2PmL3QAZp/ifeRD1OD41ni3tlQH+OK6+aL/NZPALzgmwRAKfqi4B1dZsgLV2Fw2BBlfL5g35ry0wlvFM8JM0uMO4vpMMjXJNrnbDv0WnJIhrCbX4S0REREagui11JDNJ4h5k3ILEDMcQ8xwS0QSvTkgz2d8JjAdyza68MM38dX8BqvbIuHvDOJ3l1QLjddnnEJDBDVMUvYfu7qo7DpVRD9ulaZZjAdP599ZWHQE6+2Nk33h9IG9BRGRYUQarjDhFGazGFHX8BGibNKboH/6cVEXxBfwsQUHWawhEAp9YOgXk6rY6ocWGIZlyDbSGkGcNGRfSrkNb/HjWTnovB1R9nB88vR+2rk7NrkYRG4b4D/y5M5PCgjfAJle9YUyXXxsqESAiIiIjnDGGtkisc4PjEPdclkxdgonGqE6tKzp+Zv16nHCYZ2fmGwRny+xymDJmCtGCOqyFZm9M5uegkJsfxiMuofE7J4a+jwktJUuuCkxJK99LREYOBVhlRLHW4oadkwQDXDJtDbWzH6f2tlwTqCDeWaD+7Bfgv/5RAQUNomwQFGWw1lfA85N91k/osnwmDEn7uzfAGrG55lzN8Zm0RicA4Dtx1kw/hCCmCcqoEgQEXYKd/QmwLl+2HNeC28sgqSmTwWoVYRUREZERKJlJEoQBobW0RgqSMoxDVSyXkJFyLVXpjRy26Slmv72BOduf5x1bXqEiM8znR65L1I0SMeB2KakW9Rw+d+QXqLBtRWVXF281LJ6yuKREgHEdYp5DYCxOQRbr2NBjfFA5pG9DRGS4UIBVRpSoGy0KsIIBE+n2+Afm5B6HzXu8c+NNNxYFWK0BN8ziBlmMLQiohiGZ3Rxg9dqbGtVX5epfzt2R2/7G2P14sbqN5tYGmv/+WFHAWEaoMCDY2ZjLEmgPcPanBmuJJUtg+fLco4xyJTHCYf75QURERKSvFq5ppKrVxw0t1kBbJJ7fZxxDVTT3eSHlhBhgWvObvGvTc0xqeRUz3LNXKSwRUFqDNea5GNfjQK8VU7CkvyKbYpxb+t7iERdjDIHpvJ4FaoIoB5tJpF9cwXt+cUL+eAvY3VhmTURkd1CAVUYWC0dtyuQz8oxjsE6XRlbt0aAQh8b4fiSjk7peIl8ioNKHz725L0fvdzhL7nyMSOizpgbW1MBfX/8rqT0QYH33axX5bMPQIZema+D/HTOW1RPglQmKdo0GNggJnIIf4XYAAdY+THDLJLAqg1VERERGjPNrk5xfm6TVy81vvvR++OXSGFm3YBLkOFRE2jNYnTLzoL1hbtQ+j3QoE2CNOOB57Gx+jen1f2Nc62amNr3Gezc8xXMbH8tl9prO9xhvDzb7pqBploG2CGRcjyMaqoiV6aUFubJXIiIjgdYUy4iyXzNsSHRmsK6bFuf1MR6NcR93S13+F7gF3hp3HA417UfmslyttQQYKrPF9x4iYQCeRyTIALmArWPZ/RmsIbS1L09yu7y0G5kKvAnkup8u3a0jk90uDAgKU0fHjh2cDNZdcLoEYy1WGawiIiIyohlTQZsHibB9Kb1xiEdynxdS7ZkPf54Np66BrAN2b5gcdQRYu8tgtRGmJqaS3rmBIzbuwAHG+nEiNg3Eio6vyAdYwbUZCtcTjkvHqGm1bKp7CaLVQJrCugM2m8HE4oiI7O2UwSojSug5RILiQu0RCjJY/Sw4htZoDWmvJr/ZjS2iNePnOrO7xTWI+Jd/wbvvXozr5a7dnjHanGok7e/e5T+eNUXLk/IsEJmyW8cie1gYEpjif6u7pcmV45QkvO4FHyFERERE+s8Uz7+jnovXHqBMOWUSLvaC1e8dS/qPfzXFgrc6G1YBRCMexvXYktxScp5XpilWPJpLVgmMxfotBO0lEhZug9bovjRUHEi1zX32WvaCYZ+kYVo2xtTEVGx7I2ERkb2dMlhlxImEubuwc+rh1RoYG1SwHQiaGrHZ3IQg5Y0pOiceGrYnMxxQ7ZHtErQyxsFzDMbziAaZ/PbdncFqrcWzhuZY+ULxrjuF2LhxhEFAr0ZVWKe1m3qbMnzZMCgqEWAYQIkA1+2cVO/y34LBoaAhrFWTKxERERk5AlsmgcKJFUx+oKI9e3X5suW8+xcnwIa17I23nANj85+dCsU8t9s+Fp5NU5EOaPMALJiAsZWx9uuBG7YHTC1gIBmbRDI2iUxkEY3RR3Hx+PuMEwmdCOOyr+GnUrhjyr6UiMheRRmsMqJEWjN4QZaF26CqPRY6NzWGExrHMt73sJk0AGmvuPOlaw3JtI8NAm6fsoNkFJL/f/bePE6uo7zXf+psvc4+Go1WW7IWC0vWCIMNsY09DsYsZolJ4hsSUMK9F0IWQpzkhuRCSAjcQBZ+5IbEJIQQJwFyITiEJUAMjA3CC2BrhGVkrZa1zmj26f0sVb8/Tk8v0z2LpNlVjz790fQ5dU5XT5/pU/Wt9/2+DmBZYBjYpgGWjVmR8GIgFtaDVQasTXRSsGMEdXQ010yQberASSS5KjM7oa17d2/4eEAXxVp2SIk/qeKraVzGV7ppho+ZEILJE4jlkAWn0Wg0Go1GMysqCoiWmBTBOuE5CiDrLHALpTCNWYyrFhkpwJQ+zzUp0rYibYRia8Q2EVb9WKyIylX/fkyT5kQosK5r2UhUBJiGydG2Scf5cHD1Vr507R3EPIuYB6PRLXzvueF5eW8ajUaz0GiBVbOicFL5UpRpMZMfJSx25Jp442ArKp8DIOu0VB23KnAYz3sQBMhJEayGYWAWI1hjFYMJU0FhISNY/XDA84PdG6ZMO/r8zg18ZRv800tiOqpwpbNIFgGI2huHWoYRGxqNRqPRaDQ1dHdjSJic9a/MaoE1ZpfFx569PRxJFqr2e5nMvHVxLgkjWGtT/h3bBKt+BOtY6kT1OVTA2pZE+LMBgTeOL32UqF6SD1Aca9mI50SRAnLRcBx7bDBbXVdAo9FolinaIkCzorDcgLiXRSjFgU5RivQ813wb60e+iXf2FDgOmciqquMiyiCV91CBSUFKKocTtlUUsUyLTqcBUVQ3DcANFjCCNfDZ+40+vrZlCye3Rtl5AfIWVBTwJG83l35W2Qwikaw9j2ZFEFoEzI3Aet8Hbin9PKNZhBDV+r5Sy6JQrmYRCRbWq1qj0Wg0mrngSFuY8q4ANSmCNRapnkb/sCHDzedMhDIZNl2CfH52mUGLjASeWOORdsAvrqCnRUDEsTGM+oWnVo+MsXHkDIdXrQdg6+ARGhJhW1lhEbBlloGpvucxlHHpaIjM3Fij0WiWMFpg1aw4DN/n2fYsikRp1dS1GslENiDHRxmNJEEIAlGtCo2d74fO9UgmiVZWONoQloXzyjvJHd0fvo6CvLdwwoHyfRQwEm3EKnb9WCtkbZDFAZFrJkrtg+FBjBkE1rSbBkDLsMsQKQlEdSzppXqw9uy9GA9eURNALbXCqtFoNBqNZgWSDAx2FGKY0VjV9kqLAIAh2+fB5gHuHDT4ZjzgZxGQTMKWrgXs7cUTCIVZJ4I1Go1gNW2c8rhbTn6f9szzmNKnwR1GFG2qfENgytxFdiJgPO9pgVWj0Sx7tEWAZmXR1YWXGidRGCptSrrhIxe5AVXIcaTtanYPmJiVmpCCsf5+VBAghVW1wymuPgvTwpkkyrpurSn8vOH7ZO0onmGVhGNZ7M+E3+xorIGn19xG75qXcr5vcNrT7T+2j0D6BNKnt693/vqtmRdUUBvBeslFri4GIRCVfwc6glUzExNedvpC0Wg0Gs1Sp7cXgMPtYMai7B1YzY2ZRpSoFliTker0+aSTDC2T/AAlWBbRqwCuoTDqFPWKRCOYLW01dR/Mgg9SIYD9nf38cO0Q/3ijU9ovBVgyC8y+5JcK/NCqTaPRaJY5WmDVrDj8XI6G/Mma7VIYnB/NIkUdEUpAPu/h+z4mNsfbBMfbBOJlt2OZxfaWhUO1JUDeXbjBgAp80k689DxtK1xDweQ+2W1kI518+3QGfyEtDDQLy4RFgBCh6PmKVyyIwCqEqPVg1cKZZjqK1yj1vns1Go1Go1mCfP3/2USaWxmNbeVHa3+K1dlWoj4EMgyuiMecaY+/741J7nt3F/RcTJbQwpMzFaYs1GxvaQrnHPc8Mogtw/IP0vOw8rXBJZ5RHgd++K4/w1BB6ZymDK0CtgzD1qGaQ0P8gFSd82o0Gs1yQwusmpVFTw/SEMS9QTrHn67Z/bcHH8Y16w+Ivnv+cbJ5rzoqUAhss2wREJkcweotbARrpcCKAuGPc8eJQUxZW/cqU/A5MzZ1is6edBJTCUwl6Orsmpcua+aR972PAFGODBRiYYpcUXutaRlfMx2F5jipje1kO1uQuexid0ej0Wg0mqlJp9nkxoi1ddBzTTc5ZxdBnQXCZLw2nT0Q8D9fB0dWCdjTtQCdvXxMBTFvBCvIlLL+dp87TjIWeqq6+RwPRwZxh4fJDw4itlxTdbyhYHXTuooThpmAMfd8zWtNNUpVgc94QQusGo1m+bNgHqyf+MQneOihh/jYxz5GR0dHzf6f/dmfnfW5brvtNn71V3/1svv09a9/nX/4h3+Yts373/9+rr322prtJ06c4Atf+AKHDh2iUCiwadMm3vjGN7Jnz57L7pdmbuhIH+Vs066qbTmzkYIZio6xSfdxUykyrldtEWAY2BOilWHWWAQUvAClFGIBIrOU75NyQo/VuAeGUtz8XIqN2QGOtneQtWvbj2Y9aJ36nMlgeaQvaWpRMGkxwFg4i4CqjmiLAM3UBMOD5NubAJCOjXj6SWI33rrIvdJoNBqNpj5+xOLGfDPPt1zNWLR5ynbJeLVlQFdnF2MnHwHAFMtnfD3gBAhg3WgPu89dTczLc83w6dL+WD5g1FQEpSDX2rGmXzH+FKYFKJqzB9k0FDDQsJacFSsfGQS19gmBT0pbBGg0mhXAgkSwfvGLX+Shhx6ato1t2zM+JkSsuRKzjhw5AoBpmjO+ZiX79+/nf//v/80PfvAD0uk0vu9z5MgRPvShD/Htb397TvqmmRuuHvpu1fO8iJOOJOq2NSVk8n61wCoEzkQEq2nimNXXg5IBBX9h4vdU4JNxwgGKiUBYFk3SY39HP9IIV5yr2hfyZKfxiJWGYIsbZ6sbJ+pphWy5oQB/UrS1uRAp2EJgTHLV0hYBmqkoHD4Ihig93KM/XuwuaTQajUYzJW5zOE84M02BJ9P3iMWqI1h79vbQFGmiKdI0r/2ba56LhRMIU7lc13+EzcOnsMczU7YXivCeXt6CXzkcdSIYwsBUHtf3HeBVz36Nm049DsCRNkg74aPSNkj5Phk30ONJjUaz7Jn3CNavfOUrfPazn52x3ac//elp9+dyOd75zncyNjbGbbfdNid9O3bsGAAf+MAHuOaaa2ZoHdLX18dHPvIRgiBg06ZNvOMd72Djxo08/fTTfPSjH+VTn/oUO3furBulq1l44u4QEokSBgjYfjrDuBkNV08nYUrIutUCqzAMLLO8DhG1Jq24SokbSKL2AqxU+z45K1p+riDqZojIPC8YOMLTHduqmivPJZOun46rum8n0+SwK2eCIdh8wkMFfnHVeenS/UB36eeevUvb02q+kQhUZRTBQlkE1Ii4CqnHw5opUOlU7bYFivrXaDQajeZi8ZJR8laCnNWKNUUMRbyQxTBWhtNefyTgidgoW2mmPwlB4CMy2VICnDFpkGfUiWBNRSoiWOPxmv3tmUGOtikUIhwDTJ6HBT5KKXJeQNxZ2nMRjUajmY55+wYrFArcf//9PProo7S1tTE0NJWr9ez493//d8bGxrjpppu47rrrLrt/qVSKvr4+LMti48apVygn85nPfIZCoUBTUxPvec97aGhoAGD37t380i/9Eh/72Mf4l3/5F+67777L7qPm0ikIiYOBgSRZ6CcVXVPeWVwdNSWMVeiVzQrSmTy3nY1yrLUomAoDuyJq1bIsUBJEcVAl5cJFsPo+3iQB1PZdQLHn3EGeXnWU3rUvZttwRyhejB8g23V93XMFQiEdi/NJiRKKn/vmSfzHfxJbGUvejF8T4ovqgb0QomoxYP4Q1YELirBqrkZTjzpVlFUhj4jG6jTWaDQajWbxUF6Ypj4eXU9DMQksZ9faim0er/UXBej56Mh8dm9+EPAfG3LE3ByvethASUmTqp5vSAN+41VgGhbf/Mh/wC/vrNo/EC+PP41oHMd0cAMXURwfRgI3nI9FOjnSDpZhknMgmgt/sb1Hv8uNL9hN1tUCq0ajWd7M22z885//PI8++ihbt27lT/7kTy7rXAMDA3z1q1/Ftm3e/OY3z0n/jh49CsCmTZuwbXuG1iFjY2M88cQTALz+9a8viasT3HzzzbS0tLB//35c1613Cs0C8YPYGACvPQItub4wHcUGX05voJ5JZ2vSru0K0cqwLUxV9ghSCyiwEvi4Rjjo2D1g0hRpYmsyNJW3A7CDAsn8SZIuJAoK0mmyudqqoAASxUTYYWgrq5BCi2TLiWBytOqCWQTUum/pjC7NtDhO+QEot/73kkaj0Wg0i4nywvtT3m6u2t6QP8/GkZNAmjVjx9ltTp1Cv9woFboV4bxmJoyGJo4mXawgnEOkDY/+ZEUEa6w6grUtJzCA5uwpELBtCDYP+CQzLoby2TYMmy/48M//TMatzTDUaDSa5cS8LREJIbj33nt5wxvegFknguVi+PSnP43nedxzzz1zlno/4b+6Y8eOWR9z+PDhkjfMS17ykpr9pmly/fXX88gjj3Do0CF27949J33VXDxnrDxH7DR3+TEifjlFdTr5aSw/Rjqbx6yMEjVEucgVoXG7oTwCir5LSlLwF2YwEEawlhcDxG234+x+M84fduOZCiXAlHkOlv5EfG7O1xcylFBQTDCf0MakjkJcVlQVuILFK3KFQmqFVXMxeLqQhUaj0WiWHqoYIONaoQ/rsVYYswK25M+y59wpBGFQg9F29eJ1ch5oCCx86gehCMviq58O9zXEk/D3gm+1pAgKMd70hKJ1MMfv/7+Hy+2LGSpv7lWsTofbOrMGgXmehvx5YA0oeN2zh/jCC9ZwpC00I3gkOsCt09SO0Gg0muXAvAms9957b5hOfZmcOnWKxx57jFgsxute97o56FnIhMDa39/P+973Ps6cOUM+n6e9vZ09e/Zw9913097eXnVMX18fAG1tbTX7JrjqqqsAOHv2rBZYFxEl4GAkRW4gBbJhynYCUUpvNg2TsbxH0qgsclXtwYoVCqwlAom7gBGsnlEt/jrRUOgdisFdx+DUDXlyFYcUXA8/kFXvofuBbr5w9MdkG1s413QDvhHluZbjbA20SLac8M/3QVPFApEhFkZgLf7VlFA6glUzDar2+1FHsGo0Gs080132rNfWT7NnQmD1jdBDLBCKZ+JZbgrKo+vJy8wrgoYkW58bxVDTB6NM0JLyifaFdgimrJ7vC8OgM9kJPF/cosCJ4JkuG0ce4/aTSSwZEPdymLIFWYwXaHYhqyNYNRrNMmfeLALmQlyF0HtVKcVdd91FvI5p9qUgpSwVuHriiSc4ffo069evZ/PmzQwNDfGf//mf/OZv/iYHDx6sOi6VCiMh16xZU3POCRobG4HQ1uBiGRoamvER1CnOpJmeQNUv9AQgVDhgmhgs5Qo+45GyICsmebAKc5JFgFo4iwDpudUerIZBtK0NP+aUNv3S/knCRRCQ9Squme5u2N+LNAQ917yEnLOKnN3Aj9Z08T2lV42XE0FlZoBlYRgLE8EqhJh049AOrJppqFMBTekIVo1Go9EsQZRXwBcmgRGOrd2i4nj7MyPYEpzikNqRC1DcdoHo2dtDV2cXn3ykmbgHSe8ix5J15qYDyeqRYmwow1t6Q0uBhkKamB/OvyxZFq4jUpDKV48Puh/oLj00Go1mObCkXaT7+/t57LHHsG2bV7/61XN23pMnT5LP5xFC8OY3v5m77rqr5MM6Pj7O3/zN3/DUU0/x0Y9+lL/6q78iFgtTHbzipDCRSEx57mQyCcDIyMWbnL/jHe+Ysc39999PW1vbRZ/7SsYn9PiRovZyD+QAhrEGRPj5K98jb5UFS4xqD9YwgrXCX3cBPVjf88330m6GRauSgYkwDCLRCI83pHmFG/bBNAKEClAiHPipICDrBjRG7VBc3beP3pt8jnXcwEisuer8n+x6IR8MPo94oJuevUsz2qG3r3exu7BkCET14N5cqGq2kywCFGiLAM2UqHoRrJ72KNdoNBrN0kO5LnmzPA+YsM9SqUFUvBGEgQyCFSWwVrJ9WNAQmFCcz14q73vbv3Lkiy9GIDBzBeysxAgq5mEK/HSaVZksZ5vCTSaC1Fgauu8tt/vFy+qGRqPRLDgLNCO/NP7jP/4DKSW33347zc3Nc3bejo4O3vOe9/BHf/RH3H333VVFrhobG/nt3/5tVq1axfj4OI888khpn1EUMCKRyJTnnojc1UWulg4FITFljt39kHAn78tgyoqoz8lC0aQiV8K0MGTF6uoCCqzG5MGcYeCYBgO2T354mPzwMDKfw5b5cpsgIOdVryy3+hbPdWyqOb+JIIjsmo+uzw3d3ew6PMquw6NsOTG22L1ZdPxJ3taWuXApa2KSwqr1Vc2UVFwcn3hBgQ/v+zAv/4fbFrFDGo1Go9FMge+Rt2xyxamhBKxA8dNvjTA8OkB+aJj84ADGSrQJADCL4mpXV2nTnnSSBk/QcBGRrdaa9eQGB4icGcA+P4wArjuR5WAkhZ/Pkx8d4brnchh+uuq4weExKmdVvX29OrhCo9EsK5ZsBGs2m+U73/kOAHfdddecnjuZTHL99ddPud+yLG699VYefPBBent7eeUrXwmAU6yAPJ39gSgqD5cisN5///0ztplLoXml85VPQ9yDHd+S/PLP5RE08Ph6eMmZcH9Xv+Clv/i/+di3PkV+csGgCQyDiFUdwVplESAXrsiVGUy67kwrFH/3dMHnHgZC+wsrKOCaYZT1RATrBNI0+NmhNkZj4XJx0oXR4nqBqUCancD5eX4nl45RHHXFPK3oTS5yZU51Dc81QmBUmQKoUvE/jaaGOhWJHTnzJK37XS2ln3s+evEZIRqNRnOl0727N/yhMjNJe7NOi/J9XLMcSBMIhSIMxPCVZJzCEp49zxNdXbBvX/nnWfI/XhPwiS+FP78oF3q7PhNJUxgNt4lkM54cLInZwrLIp9N07zmJIUf5s8+PsenacF8vvZf/PjQajWYBWLK3iEcffRTXdbnqqqvYuHHjgr/+RBp+f39/adtE+v/w8PCUx2UyGYBLEhx06v/c8KJsE5YaKxXiEX6AocZRrKpqJ5QiGYvQkM5AtLHuuYQQOJMjWCstAtTCFbkyZHXAuWVZmIagZ28PT727KFhIhVUZkVsRwfriHfv45OkIgTDI2VGsSd0WCITZgFJ91TsmBuN6IL6k8A2zHEqaTGKaC2cRMJk6NpsaTUgdi4DZCKwAaTc9cyONRqPRaOaICauwpBsKggpQKhxXd/WFwQgAbF6sHs4vxzYm2dPZVTvmr2MZcLQ1/P+F+aZZn3/y3f+Pzm/kraszFOwESB+ZzZLyAXd0YhY3+85rNBrNEmDJWgT0FL/YX/ayl83L+X3fn1YEHR8fB8oRqQDt7e0AXLhwYcrjRkdHAYhGo3PQS82lkgxMjrVCzgYhFVG3v267RCyCFeTZNgTbhoADB6obiOoIVmFZGGpxLALMc0Nhum3xunWc2vURpSSODP2FhRCowK+KYDVth5w9ta+SoQRKVngM2zY88kj46F58g/mYNGiUFrqqUlFgrWAhClwBxQjWCpSOYNVMjaqjvr//64Xw+2Sa75S0myZQAYEKdHELjUaj0SwM73oXhYpaDFKApDDNAVcmx65KIg2BnGbsaRmT5ildXSAE73it4B1vsGBkBPNT/4RQmVITlc+BUQ56OdqiONqi9IKrRqNZNizJCNazZ89y9OhRDMPglltumfPzf/zjH+d73/se733ve9m2bVvdNs888wwAa9asKW3bvDlcruzv7yeTydQtdnX06FGgLMZqFh/DlzjBeO0OKUnEHaxKz1KoroZpGDiTLAJe90yWJzYU2+yUFIIFimAVNkGxK2kR0O7YNW2UlNhBnrQdiho/PPME27wXlxsIKFjhkrMCru+D720MrQJMCUahgJShANv9QDdfMnwwoGEJWAq7IuCu3GoE8Lydm7H9SkZJSWBMjmheqIILoiaeQBe50kyJLH+fBsLCNZIokVrEDmk0Gs2VQW+yKEpdroflFWQroKBaYEWVIlirWOG/h7kg6SQxqaiZ0NNDctKCqdHUgi1T5OkA4OHj38QWTXhUE8jgiroONRrN8mVJCqyPPfYYANdccw0tLS0ztL54LMuiUCjwta99ra7AeujQoZLAetNNN5W2d3R0sHr1avr7+3n88cf5yZ/8yarjpJQcPHgQgE2baosIaRaOni8kYSy8qftmgClzHG+d1EhBIh7FkgWOFPdZZprdlFNdHNPAqIhiFpbF99YVONkSCkovXiAPVqUUCKdqW6SOwIpSvOG45OONYZ+VpBzBqsCwbDKRjRzoLEbsKth1IRRXASwlKMgE3Q9009vXSyAq0qEWEaUUeTOgvxh8GyFGMDyI2XqFLmRIWePBapkL5cFKyX6j1B0tsGqmonhtXLCTHO14Kb7p8P3N59gzOopVxz6gRBCA0NeVRqPRLDq9vZAuirXd3Sta3FJCUbBCD9aDHTCesCFq0tXZBTysE6iKjPzuCHxo5jn6fT8bzqkmvNRLXsBFjGQDZlCOTt3dB6uSG2hNtWNaY1CUWheszoBGo9FcJkvSImD//v0AXHfddfNy/le96lWYpsn3vvc9vvrVryIrinAcOHCAP/uzP0MpxdVXX83NN99cdeztt98OwIMPPkguVx1F9/DDDzMyMoJpmnRdhAm4Zo7p6gofTU1gmhhKVAlCGSd8CMvCisawgvyUp6qKXgUwLUzpli0FvvtdPH8BUqQ9F+lXb4o4Tk0zJSUxv/x+BJDNh4OTqDIIHAfPqhYlK3veEURAJmB/L6SWTjqOymX4u7aTVdu8548tTmeWAjJAiupr05x8rc4XQtTasGoTVs1UFO+vT67biRThotBgcjV33JCie3evTv/XaDSaecKXPr70GcuHAQfdD3Tz4h37ePGOfeUCWBdB9y8EV8T3diEWZ/egxc3jzdy787+xuXXDYndpcenpKc+t5hhhmhhWOUI46YJrJnjzIZsNkVYiQW3WlEaj0SxlllwE6/j4OMeOhcLJzp07Z2w/MDDAu971LgDe/va3z8qzdd26dbz1rW/l7//+73nggQf493//d1atWkUqlSr5q27fvp377rsPc1JU2J133slXvvIVBgYG+MAHPsDb3vY2Vq9ezaOPPsonP/lJAG6++Waam5sv4l1r5hNRTPmPeOPkrLKvz6rMMCIWr7II8KVfXqUHIpPSroVlYUqPI0WNMmcO8XIUBV8StedvdVW5LoGojliNRKcQWFsaCYqjkZybJpsvoJQiKU0yztqq9jLwWZc6TzoSDh4D20ReKJB2s9N3aLo0nXlI4ZFjo1iTRLw/+spv8yd79s3J+Zcbqk4E6+TvqvlCIGpW5nQEq2YqlFK4hsWFRBtpRwEKhSJwrsH0ji529zQajWZl0tsLLy3+rO/Rs0YBhbYOiCVh924wDEQxivJHqyHuhwPsFy1iH+eDnr098I9zLJzv6ZpVM0+U512mBGk4eIaFMn2252M8nZhhTqLRaDRLiCUnsB44cAClFJZlsX379hnbK6XwvPDGVxmJOhN33nknV111FV/84hc5dOgQzz//PA0NDbz4xS/m1ltv5cYbb8QwaiPCGhsbeec738mf//mfc/ToUX7nd36nan9HRwdvectbZt0PzfwjFEQCQSL1NKOxckTyxpHzNQLr5Nwfx64TwarKhqRGsb0bzLfAWsA3bJjI9leKSKS+RUDUrzZMDTyPwstfwfAuuJBsAyBtg68kgQp4Qf9BfrBxPROVOtfno5ww2oCB8kmEuCixtPuaovD5QHdNOtClILMZhIKEm2RNdheZSJINT/Qhpaz7d7rikUFY5GpCVN29G8tcwAjWyd3RkzfNVChJ2omXfKHLLLnhh0aj0ax8gkuwtUqnwfeviAKjCigYFeNrw+ATr/sbtq9u4Ie/fwWON6djjiJa/+/r/j9+6WtfojI9KuMkIDPG6/oaeEHEou+WuXktjUajmW8WbIbzuc99blbtbr31Vm699dZZn7ejo2PW557Mtm3b+F//639d9HF79uzhgx/8IA888EDJq1UIwU033cRb3/pWGhsbZziDZl6ZEAK7u2FfKPR1ZgV9rWOsHn+MtWMbaCyMsysSIJwIlnTJ1dEqARyr+k9EWBZCSQSSrUNGGBTwwAMUdv/+PL4hUJ5LUDngQ9VE104Qf+svkivabADg++Qsh8bA5HRz2S9JKsnmvmdIejmac2OknWakAGRAO+s4LM8D8JqfL/qwzlYs7e2Fq4p+Bt/dB3sv6q3WReYyKOB0y40IsxkEHIq3sOXEeXZtWVf/oBVshq98n2CyRcCCebCKGg9WHR2jmRIpyTixqk0CAUYUBTr1T6PRaOaJie9XUxV/2t9bvl+nl44N1NJCUcjkwAt/Z8IwiRSDLVa8D+gcj5VnG2Bhx+NYqkAgYkyo+BknAYwRkya7Cg1cd8zjddcdJmWFwVQra1Sv0WhWEjqE5BK5+uqred/73sfw8DDDw8OsXr2ahoaGxe6WZgp8oXADl4R/nqMtoXB4z5//CCEEBcPDUC5S1KbcR5xJAqtpYQqBKT0gUqqnXvDmt9CVzGUJKvqnlCJa4bn5wmtvL/2cikYxpIs0wvYqCMiYDg2Bw1i0iZgHORu+tQlO7hjmoS8I1qQG2HWhmWOtAIpmNqL4IU+vhohhs9GN0l8oDsorhGtuuaWmr/tjYwTFruXMufm9qGyWdGQ1ebu5avvB08NTC6wrmSAgCGQ5EkUIrCkE9zlH1Jp36whWzZQoRcaO1W7GABFZhA5pNBqN5mJQgIxYxKVipSdrq7NnKLRdH46vDhyAjZuJLtT46gpFxOLYMkNghGOFrA2PXf0S4t6TnGx6HoFCjTxPu+tzPppa5N5qNBrN9GiB9TJpbW2ltXVyeXrNkqCnB1rCiM1nmgNapEVrLrQMOG3nSxXolW0jVJ7AcBCTqvdMFlixwyhSJ3ARRECEsXyFYPb2FJeCTI9XRbAqKWsLcBURkSiWKuAyIbCGEayb3LaqwkgFQ5I1hwlcSXtmuOocOSsJgGU73Jlp4/WHBero8wSfvR2z90BZ2OvtrduHTFOUnCE547h198/IpOhTmU0znLi6tOlIKwgR8N2nPs/dL72WhugUIcgrFBUEBE6kZBEghFg4i4A6MYdKF7nSTIUMLQImmPCH9gIXW0R1BKtGo9HME4YC3xQEKFo+3IJIpLlGxIhKgx9HczOfgHBBP7eqgb+/Ocr1JjwqMkuzQvIcERgGnlEx9jeMckBDMrk4nVrhGLEEcZnGM1YD5Yq+j191A5mIIlk4BcDqwOHpReqjRqPRzBYtsGpWNkV/oOcivfQ6aW7K25yyc2REObIybyhMmQOzETUp9TniVEe1mk2tBKhiBGuILBa5mk/Uu36D4NrNFRumtggQTgRTFsAsRlT7PhnTxjdCkWPbcChQBpZE4VEYS+M2Zqpfz4gDgkg8ySuPQXNOofIF3HhALJ2eMiVcBQGR1lZeMRr+3g7FcyilaoTrmXjxjn3c/x/hZ/Si7m7kb/0KGWcVOZswOlaEg35bKi6kC1ecwErgE4iKz18YmBf5O75khMCY9FI6glUzFUopUk6i+pt14voR0UXokUaj0ax8Asvgp0dW0RhYvOkHBX71pePszsRpcBIAbCvEUYGPMKefCgYDfXjJGApFWw5enY/z7ScPzIn901LEtarH/cI0sacIaLjimSNLARGLk/D6GItegyz+qo+1wrYhSEU3kyycYu8BxWevkUQ9Rd7WS7MajWbpogVWzcqmePNXD3Sz/+Qj/MXnFYaCl403w9+HTbJbr8bsy9U1A4w41cKdsG3GzQBTVkRmDg3izpHA2v1AOXKz0rtIodjT5zBYDASTll0dwVoxyDEeexhTlQt3qcAndaEfv7McRQbQ0tRIU2MXKngE0XcGtpb3GRgYIo4diVQJI64ZUJvsW8Y7/RyG4+AbUXKRPbRbTbz2d+7ky3/20OxF1u5uYpt8tg8WnZiyveTTGa4bcOjtrP6YLAmjWW+KE61cVBAQVBb3MgysyarnfFHHg1XqCFbNVPzDP5CJX1P6u016YEqBUhJVx5ZFo9FoNJeP2xClyVe85UewIRcBx2bjWIyR4tdus2/xi391Bw+86zvTnsc/f4ZwVVXhGVFydhPR9LH5fwOLhDdZcBYG9pVYTHUy81jLwIjFifpDxLxBsnYzDZ7AR4GCgtXKuaY7UOyjTUGLivDdTdPNRDQajWZx0QKr5ophcnTqBFlHYMocu/uKG4KglHodidYKACdX2azOhALr4TYY6vD5wN/fzdk/3Dcv/QaQgGs6JIu6rtXUWuXBWomIRnn5kQzH2opRursCMtEYvqE40hZuyllQsAtMyMe2lyPie6BshBBsH4aCs5qUE+NkyzpGY1muGTwJfX0QBLz8LQCCwE5XGc3/7gduY68Z4VTrbUSDMEoirrZy5EKa7atn9ijuflcLHzk8xroNUdzOOEiJ75qMf/qzkAgV4GOtYf8BcplRxvLTCKwTFgbd3Sur0FUQVBe5MgzMhRJYS87DZab629Jc2SilUALGI4mq7SKcq4OYegiSDEye7AhTBW+Yxz5qNBrNSsRNRoA837gGfrEX7ulvwph0916bmjk4QP7Wb4KUHGtbTyr2IpQwCMQusq5PfLKN1jJHBQG+lNVZWoaBZeqIyflExBMI4Nf3fY8fruvmREu8FE1hKgjMZvav3QM8wa25VpwhLXhrNJqly8q6M2o0U9CztwfjjwyMOqnMGVvQWBHxGQjFRPJ1LFpbhCVjSaLeGDEftg+BUjb3/LCj7Bs6D0KeaxjIighQoZjWgzXql721VOCTicTxKv7ag8DnT+/6ID+xqY0f/r6BUpK4mwWaSm3WGjt4ZPNGfNOgrxF+uH43t515mmvbhnltNo4rJPvMsarXbgpMDnbuxDcSUNR3k9Ki9/h5tnUkZxXFKkyTm/ItBMWA20wgSFnhavXE0bGiRVMkLRnLFOqe58U79nH/kaLNwIyvurxQgU9Q6REmxMIJrKI22FtpiwBNPWRA3nTwnCiVvmplar/DJqL402ZAg2/S7tvYvr6+NBqN5qLo7AROooDBeCvj0TjJQj8ZZzUAMa9vVkujEsjaMS403EC0WME0ToKD58e58aoVVoMi8PGN6qmxbVkYC2XBdIUiIlGUgG9d7fLaw1/n2bY9DMc3VbXpa1yD4RoYSLZmHGQ+hxHVkawajWbpoQVWzRWDaZjUm+RnHWgN6tdFbYzXRrCej/hsNi6UBqZCCFKNLYzlczR5+Zr2F0NvXy9pNw2EQsOETUDBrBbTeMMbiEwlsDpREvkcxkTaduCTicXxjbIwljECksXIg1zUJFoISLgZoKm0cj8av5rVFb+Wgx3wTMcu7mpoBx7DUQZ78o0lj1XlFmjyLc40ry0ds2UYQHH04/+X/p1/TmfjzJ6LZixG2mnjq9d2EQiDzcPnERkXIrC7D55cQ+mNGAjGRsaADTXnieV9fvk14c8N8V5WUPxq6MFakbImFjCCVQhRI4vpIleaukjJUKQhzAqovGgmJqvTRLC2+TZ3DLZhKkHjcQ+Zy2LE4lO212g0Gk2IcssLz/0Nu/jG9q2cCWu+kiumLkX8YTYGZ2Y+17kzHG2/BiXMUvaQEgbnR7OwwgRW5Xt4RnV9A8suP7/v3V2ln1fUmHKREUIgFBSNwbj36RP83Y2bqhbzpTB4vv1lbBx+AqEUMj2uBVaNRrMk0QKr5oonawssmeF4cZy4bRAaJAilaIjVKZ40OIxQBlGvQN4KI1xFLMqp40O886X7oRiBVemherkUDKssSpgmhmXhTFE1XjgRon6eA6th1wUY+vZXMM0IgSGxijqYKySJSDhoTDpJgvwYcS/LtiEqokzriGZCcLRtDa9+NkrMz6MwUfkcIhYnGBpACRvPqP2dGZbFubHcjAJrppAmaFrFmZYXcaI9TCt+bGMDuwetUKQB8g5EvXLfsqk0eS8gak8q+qVgle/gC8lKC4BTQVBd5XYhPcJErUWALnKlqYcKAtJjWYjWXh8dQYSBoMCxvsN1j91TaEQ5IIVifOQ83qkTRLbvnO8uazQazbJHplPQ10dwVYSRxBY2pGvbFKxWCqpeZkEZpRSBaXBk1SYCEaZrAwglGBwaRal1F13EdCmjPB9/kgerbeup8kLgi/I4oTk/RmP+LLCuqk3ObuX51p+gNXg0vMbbVy9wLzUajWZmtImJ5ooh6SSJ+4K4Xz0Y9E2BT6buMVYd0UoKeDqeYU2qn8Nt0NsZnuPpVWvqv3B3d/kxA8H4GMr3UX550Kt8vzqCFXAizpSDWhGJsG6sHHoqhIFnWnhmki3DYVTpvYdtEsUI1vve3UUuahIr1BmBT6YopB1vu4Z/uX4dX9pmcu8/vCrs++gwhSAUOc1JekqgAsYGh2c8fWfBomBFGI4lCAy4ZgQ2DwO+H752PSHP90gVaicJLyo0c0e2jVdkVnHVaDDze1tO+D6uWSFkG2JKy4i5RyBE9eeg9VVNXYKAfEVF5qRLuG6jYFXgIJRZ97CYq2gJbDwDPAPcwMU/+/zC9Fmj0WiWOTITjud8a1V5cb4OI2LztOdRhTyDiRaydu3i+Jd++P/IeitsbBX4+JMiWG2r/n1KM7c8m3Srnq8b/T6dqfPE/KItWHGcuXG8ibXDN3Pm7nsWvpMajUYzC7TAqrmiyEVNclETurpK23r29lCImlhBnm1DEPeBIMAMph44nnNcrho5XXp+sAM+c0MHP1y/kd6+Xvad2kf3A90lP8HZ4viwIxNjfd6h93wvAOrOl5Mey1SpWPH41JGgIhIl5uXZdQESLqXCWHEPTAmGBJQi4VQPGmOFdJh6b5a3F+22yDiQLj4QcGTVdk633MSJ9tuJFc8vsxlcM0zhnXjNY63FR7NibGhkxvff6UcoWNGSECMFHGkP+xEYhBMFs7rfyvcZy1UXugrGRtjoxxiPdnK8/XbiyVfxP36la8bXXy4oGeBWiO5CGFNaRsw59SwCtMKqqYMK/FKU/2RyFownW0uWKJW05moLr4iotgfQaDSa2aAKoV3VaGxb1fZtQ+Wfw/GdxbPnRuueo/uBbu75x1eyv72jtG1C7Ir54AQwkp2myOgyRPlelQerAOwpssU0c0tvY55Rs3w9CWDL0PG6bcejjXztBS/jxGD94BiNRqNZTPRdQ6MBRmxJ3B0sb1CKF519Zsr2zzsFVqWHEBVp9B1BhK2qG8HMPqP1UFLyM8PtvGK0mTcOtXF9KhQmpFD829ZoKHDa4es1J6d+DSPRgC197GD6gW+sKLD27O3h1o23sK1hNQ2xZpIvvpmkWyHGEoqkADsvQGXwYt5uQvhtYXTu7/4OvjmFCCIglZ3ZnzYmLPJW+b0daw2FmEr5rsGOkygMlDf4PuP56vea/e5DFMw4p1tuIme3knFWgfMTjAzOLPIuB6TnVVsxGMbCTQLqBMNI7cGqqUcQkJsksJoqvIQCAVv8LSS95tK+7ne10Ht0H88/11t1jLag0Gg0mtmj3Dw5y8G1mmv2xbxQIJ0w+znw/GBNG4Cxxx8h+OH3GYk01t3vBIrRrFt333JF+X61/RJgL1QB0SucazZcz59vG2VwpB83F2bhJQrDU6ZIKUPwbP/4QnZRo9FoZoUWWDVXDF2dXSSdJEknWbPvbNRjVfpZTjZlyFrQlDvNtmj9CNakk0QIiPUPEvXG2HUh3L5lGDYVovz1v7ew86xP+ol9F9W/oP8czb6FE4SRAW90N6M8DwW4Vlm4FFLRHKstvlXab9sYeZeonytvVApjouaVUMRWtVXbH/T00HDfOxFdu0vtM8Vo1Xok3FAosRCooC085OTzuGaCgx1hZERukm1VOu/PKJQkzRgFK1KKnJ3AlJT6r8wMWTtF2oGMI1C+z3i+wlJBKVQ+x2j8KpQoR7sqYXLoxNlpX3+54LqTLBGEWLgIVgTGJH9eNataxJorDilr0i0nMBXEA4M7s7eiKrIFAhXgBPDKY2DL8GEIAf7KipTSaDSa+UK5LjkrWhrCGXLK4RwjqSz5yan+3d0YUpEIDEajzXWPcwIYXmECK75X68FasXjds7en9NDMLT17e3j0bwO6zkk2DIVjXFv6NOXDQmxxPyzQJg040gb71gX8+lf/cMoMqokswovNJNRoNJrLRQusGg0wbAdE/XG2XvgGr3vmP7jxzA+xVW2aKkDPgS5MCcL1aE9P8gVUcHR7Nw3GNajAZ/Sxh2fdh2BsFAG4ZvjoS/cRjA1zaOhZcmasJGEJpUhGpjfdF65PxC9wsIOS4AlhROiTayHx679ac0w0EuHI2e9woP8AgVH2SwwtBaZ4IaXwZWtoq2gIClaitCsyqbKU9H2y7vR+XbYSFKwponNV+HpvuulniONiGRamYaIK+ZJFQPcD3dz999383YP/m4zTRtomnFUUZxYXRrP1z73McCfbVxjGlEXP5px6Ra50BKumDvf+v59mxJahtUgFsYr1AWmtI33mFADp3BhKKSIyvMLe9DS05qAjJfk/Pe9fqG5rNBrNska5bl3fVAhtArYPCZqiTexevRvlewykC9WNentBQUQ18uwqu+o7fNtQ+Nj6fJqR0Vl4988jcy2iTS4gKgBLR7AuPLI8/1o79hSrx58mO6l+rqMEKEjXqcGg0Wg0i4kWWDUaYOP6nRjCCAdTKhSvppOMuvoFP24NaM2eIO5OSn0Xgqujd9HM2ovqg8rXin8ql0MYBluGY+WNr7m7xj91MsLzifrV/ZrQGROBSbzO8cJxiMtU3fO1ZY4DMtQ4RbiCHB4kMKTNQKQBZQi6+pPcfFpwwwWLm65/TfV78X1S+emj0H7rzIYagTWoGNsaSnHdto3YMkMgAwIZ0HvuScbS5WjdiK9QErJOK6YKI2lzFqRsxWC6sCL8QguTIlgNw8Q2F2gSUE9gXQG/U83cYygIpohgreTUuaGq5+9/xMCetIZgB/oa02g0mtmgvAIZO0bSq75bd6b62Nn3NJtTY+xevbvY1qM/Vag5R0Ng0hDbUHpuKkjb4ZgsEGG2wfBoakWMqUr4Pn5F5hMKLO3BuuBMWJEJwFABHeljXDV6uqqNoQSOEgxlVlgUtUajWfZMHwan0aww7nt3F0BNek9QR5yackjV00NPSwtPRUcZMPO8+tQTPNNxW1UToQQt0ZcwVHgQgO7dveGOB7qnTC2SmTSdacH5ZHFkYYPMZVDCJD8RiSAEwrJmjGA1XB/Hrx4w7+4r7mtqmkJgjZBPHSGdbK7aHvfByw+yfuQEprqTuFedbmYFcPr8EFtcl4wTL/kliUiEjFPxew0CxvM+a5qm7XpZYC0eeufxfppzBkoIrrtwnNUb12HIamP7bDqD64cr3lFfkbUbS1EIE9G7AkGh4JIq+DRGJy2FLzNcrzq62rEMxDSVgueamoCOFTS/0swdpgIpqr9J/9uPDvNsx3akmLhsFP1Zn+sq2tS7ls+PnZnPrmo0Gs2KQb3vfQw5LeF4rLgo/kzkWf77U98i3tjC518SI/H97wAgAsWFLe+pOt6L2rwy08GTbTdSWdzdkhmS7kSmkiKXzZH3ZMnTf7mjAr/aIuCWWxdu8VoDFMcFQYAlw/mFUDAYV7z0zGEM1Ulj3sYsDoHX5u0qi7Aq9vcC8JEvpOEfixHOPdraQaPRzD96WU6jIRRcf+XC1diyHJ3nyGkGjF1dCCF4KjJKS+oCDfkLNb6hcWMNRHYAkHbTpN00vX29U55SZtPsfVqwKmOQ8NrxjBgql2NcgCr2SqEQlk3CmcEiQCqa8vWra4rbbydZ53jhRLDyp7j74JFScSsAtzDKa39wgo1jKV5/8IvcdvwxmvNlY3lDwWiskUw0TiAMEAKSSYQTIeKPoIr/vn/6MUZz00ewKhQFK8KW4dDTFqAj1cftJ77LHce/w5bh8wjTJGuGURMKRdpNowr50rmjPqSjq+uf33NXxGp3wa8eUDrWwk1uhBBM1r90BKumHoZUJYE1MMKCfc25MXb2PVO2PEEwmikvBuVsQAhcy2b/2hfz49Vv4Hj7HUDzQndfo9Foli1jkQZ294crWUa2wGn7PABSBljeCEopjrUovr/a44Pf+UjpOKUU+bhJwW4uLcwfaw1tozY5XlVxVzyXzGRP+OVMEOA7EbCs8LGQBUQ1ABxpFxxLeDzXFICAu49CWxZacynWjj5c1bbNNUlncvVPpNFoNIuEvmtorhhmMqePPPgVhiIBX77W4C935TC/9fA0J+sB02QgKtnw7AXU+MOsSl8gOUk/jMVuwp1FiiyAymbwlcGJ9pdxov1lnGp8OSeGMhQiCaQRRiBIAaZjE7Wn/9NtfNHNbBnPl1K5shblAaNp0tlY680lHAcBXN93gFtOfp+oN05rdpg9Bx9CjY/wvJXDVJKYd55Nw8+F5y5Kv+/vTtK7dk2VyOzEoiTlSOl5Lp9iOFObhlZ6/0qhBPhGtFTU6pzt4kxYHfgB0Q1bAGhrW4cjM6UquMr3GSkWW3B8GEpsqTjxxH+Kbx75OoOTvcaWGd0PdPO3P/gEabOcQ+0sWIGrkMkmASsqRVAzZ0yOYHWRDI7301goW5EoFONF65BOz2F3Oo7lRDjetoUzTRtQwiBvNzOcuFl7rWk0Gs0sUCgykXjFBsWWgfB7V0rJq45lSnfxlsBmcHS0dB9X2QzSNMg4q2rOm01cIOqFYzJBOPbKzOCtv5xQfnUEqxACW3uwLhhmY1Pp8cXWYbxsaJ02FAfPUHSmU+zqe7ZY5Da8BkcHBqc830e+kGbLQBB6Cms0Gs0CoQVWjaaI2djMX264wB9vOsdn1o3OnHKdTIIQHG2DP/6PYW4/sY94odpLcI0f5c5tR1G+j/J9tpwYm/J06mN/xcHObWSd1nCDEOzrLzAeb61+2Vhk5r719NDWnCw9lQakzYC0GWBaVl2BFbtYxUApNg2fYfuFb3LbsW/TmroASvFkbIxM33ky/X00FDIoEfqxXn94lC2ZJoYn9XNjeyNrHQdR/GcgGJ5O3CxWCT+4LsHxDosTm5px163BH+rjuD/MOcvF6HkYgPfc+QHueWaMm08pXnLSR/leKYLV8mMERoTtQ9DVBzf0iVIfbCmWvcAKgKqOQF7ICFagJoJVC6yaehgKggo/OwUIGdDYf6Gq3deP9eCPjtCdaeP2sdBDZDDRPulsDieH6kflazQajaZMYBhVRa7GIpA3woJUSgbEvGrP/3UFh6wXCqUyHWYo+WZjVZusyPKnd/8f4l6+vMTq+2RXVASrX1XkCsPQHqyLxIjlUxgfw8tmedPTsDoNTgDJQvW1m0pNHcH6P1/lc7hVsT86qkVWjUazYOi7hkZTQS5mkouGj5m4791dPL3WJGvD0WbJ2mf7edej+6ti++LSQJnNNAcWTcHUaf3K95ECDnVcU7W9kCtwouPaqm3JRP3KsJOJ//7vYaoCk4INaY1bmHVW5I1I+byuGYohSlV4BSjFL98Nv/JqhWtUCB0KlBGlP7k6rPJpmrB7N2s6monK6gqz6Uyu5JU6GeW5SATXZVvZnU6ye/VupAEtwiBuxdjT2VXV16Z8RUEuPyhFsJ4fGSr5M0GxPxPHAUPjtcXElhsX0iMEQpWiWCP2wgqsky8fbRGw8piL6symrI5gVQJMGRCfZF9iSEHqueNYCGwZ/p1mrXhVGwGMZyYVFNRoNBpNDSkrUrKWshAIIfjTb2RIuGAXAtaN5zEqxnfrjeZShoBMpyAaIeuUBVYpFClxCBGNEfHdcHxIOHYtTDGmW46owMevzDozDCwdwbpg7OnsKj0A3v5auO/mbNUYM+lWjx/Gp7AISOemDmjRaDSa+UQLrBrNZZB0kgjLwmxsQgSS9tOn6EydL+03EFwbrOeuzCpemVnFdflk3fOoQp5xO4Zr2jXb83aiYgM0tMxQJWritRMJ4u4w24bColQTxafa45H6B0xEsAL9yVAM+ehLBXe+1cI0Lb7zzxYNLiR8QaxQG0lWsMrnFaZJZ1sjb9vz3zANE7M4YFW+N2War/I88pPe/y90vYUb27ZXiasAIhqlKT/O0x3wdAd85dAXGcl5pB99hDc9Fb6PY61wvBWe2xCrOjadyeEFy3dC8P7376MpJat8cp0FFlgnWwRIqQVWTS2GAmmUhxkShaEk0UK+ysfPVDB66JmKdoKcXf13CzCmBVaNRqOZkbRVvRBv+wXsIMzykb6HgKooVltCKhcuUiu3gAIKVkNp/3ErQ1qMYMQS2IHHwQ442AHfPvJ13GU8nqrBnySwCqE9WBcJpRR/+TV430Me6aELWIOjPBIbouXCQFU7t+BS8Ke3qdAjVI1Gs5BMXylHo9FcFFYqx6r0BWBNaVvXUCunixrpKiOB8n2EVf2npwp5xsZy0ADpos4pZThgaAgs0oZf2paMzy6C1YglSLgDoMp9UUrR0VT/eOE4NdvWFSJ0pZPs2dIFWwDxCJgmX7jWI2flCIxQBEk7VPmvNkRM2pMR0okEpswTGOFrKs8jVfBpTdS+lvJc8j/3C5AJBWRhmBjCIPLQN2py0o1kI435VKkQ1qYxRSrv0RxEyccaS4UZBHAyOcQIDhOhvMrzGMt5tCenEJqXOLF8ACL8/U2k5kfshf0qnxzQoS0CVhjd3bC7N/x5T9cln8ZUqjqCFTBkgOX7mDKHb4ZRqtl8igtmAhwHKFCwIkjDIO5N+LiKYrvpi+RpNBqNBnKmU1pUB0EUH0yTwPDJ2GCmc8TdHDkrXPSX+TFSY2noaES5BTJ2DCksjrTBuOmTMxVJfxyjsYnHX9jKeBB+J9tK4BUWr3DodIVjL4UwgrXCg9UwsE0dwboYhFZoRV9gKTFTOQZNsApFqwsgkD4qCEjlfSLJ6kADoeCF+UZi7RFkLofK1yT0aTQazbygl+U0msvkdW9Lct+7u0J/QaB5qK9qfyrSQEdGsDoDqzMGcny05hyqkCdbJ2JrMtLzSTizE9NEPEEy318VKWZJn6taE3XbG7E4G5s2IorvwzPAWrchFFh6ekqFvQByRoAVTO2HuDZhI4TAiCWwZDl956s//g/e/OD/qJ927HnkVcVXkmkStQ2MOn6zRlNrVaEcgGB8lGv9NWSd8vsTpolnZzGDLApFIAPwXMaWu1AjqgXqhRZYJ3sAa4FVUw9R48EaRrAiJTEvVQ4rkZJ/ePZh9h6A9izk7EZQkLPChZsJy5aCu8z/bjUajWYBKIyOknaKi98oHD/0nn/76wR3vtVEDI4S87LEfIj5YBomqbHQe1W5BcYj5WyrF55V3HPIp6d3O0ayAZPqLKRCbgVVcQ+CGg9WHcG6OGwfMUm4kJik359PBlXzCnyfvvHa7JYbco20WAnaPYsOqwFfLt5CgEajubLQdw2NpoJbNt5SesxEz94eujq76Cqmrx9pFxxpF8Ryo+RswocFvjBwzbJ4KlO1vkCykCdTLEgw3QqrkgHJyOzENCMWp2lNG9f1/4hIUCBZSHHj6R8QT8Trthe2Q1+6DzsAO4DXHoHfvO3d9OztqWmbExJLFtPL6nS4oWhDIOLxqoGQpRRK1Y+gVZ5LXlWczDCITpH6bjQ20ahMIn6GwAgFPpXN4JitjEabCYxQmBHJBqSZwwpCMVahShGsyxqj2krBnqXoPmcvP9kiQOurmjoYsnqI4RmgCBCBJOKNlLYLBQWacIsT23ONa+ueL1dY5n+3Go1GM8+owKcgq+/Rtu+SdJJ88uEmbtl4C7ZhcfVAClOWLX/GxsNFc+UWGI02YCLYPgSNBcWOC6lw8d20kKI6HdvNrxzhStZYBGgP1sXi2FVJpCGQhiDhwomWcE6lBES9sk2ACnz6UrXFa3e45UWCf9od47df13xZnvIajUYzW7RFgEYzR7xo220AHDj1fUzpEhgOuy6E+uPxVd04foamwklktiLyszu82atjh8jsuAkIV2u3DIMSiuD0dzArI8CUojlWLa5NhdHcCsCm4eNsGzwOCgzDQMTrR7ACnIpVCxjOC3ZXN0iGA5ZcoEoRrMVMsfIqswpoKBbiMuIJbFnh8xUAcgqB1XWrIliFaRKbQmAVQmCt2UBjfpwfdYbv5/mnv8Eb+q6id1VZzI7d/TrUMz/AliYT69vKcxnPL9+qt4ZUKKrtDaILHMGKqFZUdQTrCqH4fURvL+yetuWsEKpaYJUCrMDj5W9W3J4/xmBDWMBPoUjnxhmNNqDI8Fzr5rrncz2fQKq6Rfo0Go1GA6pQwDVtjodDQHKO4DN3r+Ez8S569vbQA/CuFlqLUa0TGQJ943mkUijXZcyMglIcbxNcrWxarloL/YcBuGfXT/HQ1x8pvlqAm68Vt5Yrvu8jG8rFvXQE6+LR1dmF2dgLgBodLW3/w1fFaA+GCIyN4T7fZzhTfQ2qwGfvIYc/uSHP9656MYc6N7BlUPL2f/wR/GN3mJGn0Wg084S+a2g0l0HP3p7SYwKpJHYQplodaw2zYH0jQtZp5WzTC7kwkq45jzIEWacsDE4EchpSQTARLaAwfZfG6CwjWKMx+tJ9KKAlC6056HQdRGRqD9cfNuVQMixYEOTzWGs3VL/fj47Q89ER1K5d3HMoQ9KtH3HbmAzfi4glUN4Y0bxPNO+TSQ2RP3GO9BP76H5XS/XvwHPJVUWwTi2wApif/BRN+VFyVriqnXbTPNeyvqpNpKmZj9z9Iayg/Dv/zokexpezRYAQyEkRrI4zO9F9rphs26AFVk099n7xdBhdXnx+wznYNuQTCBDKxfbHMSWYMryGjjdESUXaATjSFhapy1Re2jKYsZiFRqPRXMkot4BnVtsIBeak782uLta2xJEVi1Wu65HK+6hffjujkSS7+2D3eUV7rI2WisrtziS/ftddORGsrheQdtOlhxAGEUtPlReMCTuyogB637u7uO/dXdXXqVBE5Xjp+fdOfZfRTAFfloutqaJtRc5ZzcnW4jzGMDi4agtZc2HHyxqN5spDR7BqNHOMgcDxR8nb7eQsJimQgmcHs2wA6O5m/7F9bBkIEM2NVR6sR1rLR2wfKgoUUtExeq7G/3I6DjYUuBfoT4arKVc3r5n2+P5oQHZgGCEEIpBTts3b0JYZCIXN2npVNLaF4qkRT3Dv02N8uivcbkvAbCJQkrRbLTQrzyUvqyNYp7IIADBb2mnKjxLzQjsG5fukK4oRyCBgVWsDRkJU+cXagWIst3wjWCPNLWwdcrAnVCsDopGFHTBOvi6kFlg1dfANmyPtgIItI2CZNlEV+v0VDIkdpCgY5WihVKSRocQa1tWuQQGgggA3UNQ3OdFoNBqNcgsUrPKYwEeBqB3zJHwXUxZg4hvVDxjJubQagrFYY1XbZrecieQ4drj4X8T1lvmiV3c5bdyNWRzfHmZW0NTEi3QE66JRGbjyw98vfwYFQ2IXygJrIAOU5zGaLRevlfnwes04q6vOaUfjnIs1s2U+O67RaK54tMCq0VRQz2909geHx6rr2mgonGI8Vr6FT6TR56Mm5zPVA10FBKZFOlIrG5iVupWSrPbHa9pMh7dtK53/8SwY4eAk0jC1YDnBtkEFKBrl1F8POUsQ93JEvPMo1lTta8+O07g+XDEWsTgNFQMhWwkaPJNN2dWMq2G6H+gu/c7V5CJXhkHMnnpgazY105iv9rNVKMyJRexAsirhYCSSWDLN7mLtMctIkXU9XF/iLLPIBJnPYUQj+KbFRAyuEAGR2MwF0uaSybq71ldXDt27e/nIsTRpF5JOcuYDpiGjit91Ag6utbjjxT9D0+lnSDq9FHwVCqx28ftRwLPt15COAhUCqxLhBAoAKXF9OfllNBqNRlNk77/eyyvt9aXCq4FtgqjN2hEIIn6KdFFgVX4oUsXtCIWKCFhxzz003/ZeKNpTTR43ucHyFli7r9lX+vmT39+G2rYdgExuDMsytCXNEuLvvgx7rt3N7+54HlPmCIxw7KsCn2yF0K8KoSmYZ4ULBVuGw2JuMQxOD41pgVWj0cwry0td0GiWAb/927sQagyhMnX3Z10/9Lnq7cVMxJHrVzPU3oEqhroKYNtw+JjgaBv8sBP+v//eeVF9ydsC59QF7L5h7DMDON/+zozHFLWOCmuCWv5t71cBaM30kqhIHdt86jC3PfwFDCsUZ414kkjgYslwsJNwBVd7cTbKtdySa2XbQIXY7HuTLAIM4jMUb0rmMuy84BLzYHc/xCvmEEpKOhqiGI3N2EGW4y2K461wuClAFQoMZpafb1gw2I9n2gSGqPigIBqtE0Y8j2iLgBVOEISP9BShpLNETkrFs61JUSjF4nMX0y9XWwRoNBrNlMQ9CCpshFyjTgRrTw/ik/+AFykXIVW+x3DWJR2t9ukXpkVDhTWVbVUv1HuLuOj1pb9Llx6XStoMSg/XmmS/ZM0clKBZWASCtua1mLJsTaHcAvlKgdULJwO+URt8cGL1VTrrSqPRzCs6glWjmWMCQ+CZYCiXQNQWlPK8gPGcRyJqYcdtHrgGdgyV07F290He8vGN8p/ntiFYN+rylj//ykX3RyiFyM1OTOzq7AIenvmc0TDiIebnePnRb1AQEX5+fwHn+T7MoDxwEfEESipsf5xCJErOhqQLz3TuoSkXI3n0bDEqM4byvZoiV9FpIlgBtrds5fH8KNDBhNHjsdawmM5T7Sb/qymKsAxk1OSms6MUrNC6QP7fj3Leclk7/PyyMruX6RSeUWsHsNAeYZMDOqQeq2rqYFRGwQcBdnHhpedAF7+14SCvOpXn726sPS7mQ1c/rE6N8OXtVikKRcmAQqAjWDUajWYq4p4i65QLYcbjTXzijR9mXXO12GREY9gV/vT4PuPZApmKegAIQSLmVC2q2qaBqPC+8qVCKXVR9lVLFdd2Su8sGZg4lp4mLwVyUZNYviig9vQw/v5bMPKFYsaaQuWy5L2KsYHvF+tfhHOVwACjuDtnW2TdgGREf7YajWZ+0BGsGs0c07O3By9i0ZbeX9q2bTgMNkw6SZ46+wMGMy5BxKYtF24fjTVXnaM9M8Da8bNAWOjleCvc+H/efdED2J69PfxoNaXHXCEMA6Mi4vbuIwUEVImrEHp12nmPyCRrg8CMMJzczbnGbjJnTwMg3XoWAdNHDxif/gwntjtkImFEpyGhqw9e2Cd4313vJmqbCCF4PubRnh4oHSctgyMpP6yWvoyQ6RSeFanaZsgAa4FT2AQ6gnVFY5rhI3l5FgHStNg2CNsGYWe/wrKtUgGLk6sjRP1c3eMMGT52XDiKoSrC0oNAWwRoNBrNNMQ8cE2bXRdgd7/gjq131fWzF9EYtswQyIBABvSee5KxVK5aYDVNkvHqMYdjmxxuU6WHlBIvWAFjABX+3ipxZhiDahaOXNQkFw0/j9GowJQFdl6AnRdAff7z5CuyW5TvUzAdlKj+/AQgDYP0G35qIbuu0WiuMLTAqtHMA3/x2r8mKsfpHPsejl9dYdVQMJjKIYvG+Vm7mWNt1Y5ALblRelc9xc3Pn2FX3zj/80eH2LZlwyX1Je6L0mM+GY7BkXbBkfbq19k+KIh5I3WP8c0YB54Lhc+861M1RjdMEjNYBIhEkjb/XPhEVfynFFvXNJfaNa29hvbMIFkLelfDDzZanOhs5mvrm6sKHCx1VC5Lk1ttB2DkMgseOTL55XS61cpiy0BArHD5FgHBpHRL2y7/PaesgKhfjKwvXk8TV5E0wkd7ph9DlVNblZS4K2Eir9FoNPNE5EQfGVuQtSBtK4Tj1M0GEtEYdpBBFf+N5kfJZbKMRRqq2jXEo1XPHctEVPxDSjy5/Be+TMCdlCEUiSys/ZJmdmRtgakqLAIMMSmC1SPjxBCUC/Ee6Ax/TjuQnhSooNFoNHOJFlg1mnnAaGxm7wHYPNLPaw99hWsvHMasiPobTeWQxbTu51tvAsriAkLQOXYGU3m86Mz36T7+Ta7rO44ZvbRCRmZjU+kxl2xp3AyUtBEKqfGqFebS6++4Dtc/iy1rq9gCnBwuVvt0q/cbhkncmSGCtaEJYeRpyB+v2m77HttXlycJn3rb12hPD5HwZLlwmICn9ryG8WWUAqbcPBeayz68KggwshdX+Gwu0B6sKxwVpnz+xVd93v/+fZe8CBFU2JwYEmy7PHkdtyRW4FUJqJW8/Oh3iAQejvQwDRPTMLUHq0aj0cyAXb2mj3AidW2ERCTK3sdHSBYIHy7IQp6+ZFu5jVIko5MXyiaNy5RaVB/WueB0s+BCk0nGqXivySQRp9aSSbPwvO5tydID4BP/7TMY0i1l+D3ZKcnly1ZoyvfI2FPPmTKmFlg1Gs38sXyUBY1mGeHs2IUf+EysYcTdDAGKnBtGhKWzeZRp8N2rrqNghT6t0gBTwqr8GA2F6uIv0ZHsJfdlT2fXJR87Hc6DX2L0d3egMJC+j5/NQrROGlrPw5z47wmuG+0laOli8tfO1448wuvyt5N2AzhwoLQ9fs01NUJezbmFYDhm0DxymJi3BggHVF0nniZSUZzAiCcwsuNcNXKWrL2BY60gVChqP95+Fa+41F/CAqPcAsOxJkwZ9l16HhGvfpr1vFIjsC58FzTzh4zYXO00wCoge3GFqLofCMVYoRS/YVZH/zgVEenNV+0AdRhT5vHMaiuC5twYq9ODAPh4+BOLM0riag9WjUajqYtSCt+KY1fckx3HxjLqCKxCYMsAO8jhmcVq7IU8Y21rytkLt3fXeFWapo1QQTn9WqpFzSwoeXNeIi/ONvIPT4VenecaqsewjhZYlyRGazuOLJAqXpoCyI6NA+uA0CIgWxRYD6wOi+BWknZ0ZLJGo5k/tMCq0cwDZkMT6cELPLUjxh1jEG/MV4lS6Wwez7K40HBNzbE3u30M5fP8OC4JfJtCIYd97YsXrO/HOsKvhT3p6f0XzaYWHlg9wJuHDQLfJylDj9l6PHZNhJf2Pk+HcZZfeLqZP/zJ20qhr7lciv7xAjm3OiU5HpndwHYwIUjIHHcc/zY5ex1xN8eOIz+uaTfqZdk68CQHOzewZTgspANwTMT4SakwF9jH9FJQH/kI48mtJN1i2pNUNKWHFrwfk39VOoJ15SAUuKtbWF8wsSOQsEwUiov963B88Cqiw4UQ2JFy1EjBFqTHhrGCPNjh98aBTkDB6lTZL7nSg1VJSWGZR0ppNBrNvOG5SMOBCs1xujR3JSVOkCkLrPl8uKPovy0sq0ZgFaaJqXz8osCqFnHhK5YPMCaqbHZ3X3TRUpkaZ7ubBML+u5bD7v7i3e7arqpFQc3iMfK71TZjRiRKNJ+DYh1hBeSyFcEGvkdGWOWx8iROxdu4ef66q9FornC0RYBGM08opXjOzuJns0Rz1V6G2XSW/mQrXtFQP+aHKbQvPPcMbYUsKMVDLWOMjwySz2UWo/uzomAoAj9UKt/1asF97+7ivnd31bTbvm43+yNj5EyXjswQjfnzJU8EUwn6xnNkvOoohMQsBdaPv/Ob/Pfv+8T8AtcOnODqwdPY2UJNu/e+9yfIFzL85NH/qtruGyYDqUWIAr0EJIp0JF4S6xXQOXJ6wftR68G64F3QzBPNvomqiP62bYdL+XgjgcI3qien9qSJvu+5eDIMLTElxYrAsH7sfKmNUeGzhlK6yJVGo9FMgXJdAqrHTrHo1ALrVRkbOyhnSMlH95V37t4NdQRWDKO6+KCSeIEMBc6JxzLBPXkUUTGAyVt2qcijMEycOtYKmqXBudbqkUlhlhYBh9vgE6vHuP1Tr5zX/i0m3Q90lx4ajWbh0XcOjWYBiLvVIqkq5DnT2AmmiawQq67rO4qhQqFSWBYIgWVc5gp6sWr3bFb2e/b2sGfLLezZcgt0dV3e60467zkrzz+1X+BY/gKrMkMlL1QlAy6M5cj61YOlRHR2AqtwIhQGBnHHxlHjKUT/EKKp1m+2Z28PZgDm8DkaCtW+pf1DF5cGvRgo3ydtRwgqqqIqpYjnF96DdXJRLVX07NQsfxxVOyxQ4uI/W8cHWfndpVRVkasJNg4+i+OXr+GG/Ak60+UIVrPCo/XbJ76pLQI0Go1mCpRbwDeqBdXpfESNq67G8Sct4qfT4ePAAYRp0zBZYDVNjEpPfbl4C1+GDLMrLibDolJ8Cgb6qva5ZsXvyjCImHqavFRZ1dJR9Tyfd0vjUOX7JYE16daOWQ0Uhy5Uf/YajUYzV+jcB41mnpCG4EufDW/29nWbKDgBYZ1SUIUCpxs7q9pvHj6F5ftEpEnP3qIY+u3ls/r41X81afjk1CJuz8nbeHFsH7/zSsELxShBcdxqGiZD4xmCSTZa8WmiLiYjBVx3IhMOtIWAm7rqtrvv3V28730P05YZ5ptbGgEQFmwbTrF78+pZv95iILNp0lb1irwZeES8/IL3xUBAVVyjQnFxkxzNEqO7G3p7iWz0geoq0hLF9OXmaokECl+UhxjKNLAneUsoATmRY+Pww+TtFgxZQFC9YFBZKdhQVFcK1mg0Gk0JmUljiAiyOL4ypCI2uShVBYYS2EGWXFFXHCuM8Vx7eG+XYpTbonZNFKcwLfJRRW7ivErVLnxVRrFeZNr+xfI/Xxv+f7xjHyPTN61BplPsfVpwPh6+Z9+smBYbBrYWWJcsgVk9aZBBgBcoHEuA75Nx4qV9B9eYRPM+MR9MBRFfYBCffEqNRqOZE/SdQ6OZJ0zD5OBai4NrLcTv/z62zLLzfMDO8wHqe98lXXHzF37AmueeJ3nyQlG8KnIR0aeLgWmUB+4NU/ivTsZDESODJQwEAl/6nP67v+DYY9+nUmVNxC7ehF5BmN41xe+rZ28PSU/QkhkgEBAYoT9T/+ilFxFbKFQ2Q2qsHGmiUMTzqUURNY06frVSR7CuCCKy+rP92mbF27cdmV2qWXc37O+F/b04ARSsikq9CqKTJvoCwXu/6bEm7RPzBogEobg6FId+I0+/lyJS0R0pA/7yib++1Lem0Wg0Kxr/wnledi5eslxRUhKxp57qmUWBdYJjLSocGxW/d5sS0ToHmZiqwoZJSfJeAL295cdyQClkNk3WjvI3N93Eh1/2Bs40Npd2C8Mkoi0CliyB4Vc9f+S5npLQH3geOSfK7n7B7n5B0kkS9cZKbQ1Y0QJrb19v6aHRaBYefefQaOaJpJMsPYRlYcmyz6eCUEwMAgwFKVMyYA3Q28mSFVPnDAE5yyMSjJNwFckCfH+94HCTT8pWpIuDpkQsMsOJyuSiJoERCqYTxRmm4kXbbmPXSAZThQIPQDabo+BfXiXa+Ua5BVLCAaVAKZRcHHsAqPVgRcElGXVqlhzRYuiTa06U/VA4cvYy/ke+kOZLHx/DeqoXQ0WI+9DgCZrufG3dSCojE0Zg/2Iv7D0giPiQHxslPzpC4BaqPFgNBefHh7QdhUaj0dRBZVIUTIcjbXCkDf5rq0nUmiaC9Zvfxpb1LZJMBE0NtSKUME0MWf5eVlKS9yWMjZUfC0hTYNEa2Bc9Bjl87kf845Of4sEddzAWXYcUk6bEpqkjWJcw//e1/xdhGkhDIA2BIQmFfiDrBlWfZ9eaF5L0ygVhTQQmtVZiGo1GMxdoiwCNZp6oLPb0VcvmziNjnE+uAuBAW5qd4+XRoJKSxszFJjctM3p6OPbhFgBe0CeJ+CO4JNk2BKrNZtsQxCcWpA2Ix+pETkzBH/zBLXzkQ70A7OnsmrF946pWpBEKswCyUCCV94kkLzYReuFQrks6kqjcQqKQnrL9fFIjsKJ0oatlykRkanrHPv7uGBhGQ02bSB1f1umIuQonEOTs8t+wME3iTu3flwgkh5wUryzaEgRCERSK0VFKYVYWUyG0p/Clwja1IYVGo9FUIjNpLkQNtg2H3483df/CtFGYwjQpiCzN+RQFq6FGpGxurrNgbZpV1i0oVRK2Fho7keSVmfDekRivLW5aj4mx4n0vy3H9c4oftTol0yNpQFqE76XRMKa1V9AsLkYkiql8gqKBkakUhaIXcMatjm41HYtkoR/YHB6rBBYtKKVq/Fk1Go3mctECq0azAAjLJlZIkwv1RZRfffOPFtJE/DxLwsXyIiJow+jcosh3EUWxUlZAR+4COBsAsIoCTrb4jSSETyIyuyJXF01PD86BH+B95hMExci87xx9iP83/jFMKyyu0/OP1e2XAspzGY+UJztCQUMhU2XTsFAYk69TpS0Cli37e8P/g9AjekJMdYJyistk24Bp8X2yNgizgXHLBiFIBiaYdapRF/mxncYdDcCx+Xxblt8fCCdJjRh8VlV/VxpK4AcKPe/VaDSaamQuS8GwUagwQ8d2aqxZJlPYvoXE6X4KVgOmgi3D4fakNGlub61pLwyTVdEGvAlbKCnJ5wvkW5NkbTBT2QWJDVRSYjc2MFQMst2RjSGzGYx4YvoDi/zFNxQnd6yZctQtDIPGqJ4mL1WEbYcZLiKsTWAqSgJrepJXezJiM+aNlp43KAsBpAs+DbMsqLucSLuLE3yh0WhC9J1Do5knSoWqgPz+x4m7mSnbJnMjKAEsglh2OXR1dvEHfxD+XPl+p2Lkd8Mo3fyBH/Lev/tF3Cnq1QgZEJ3GN+xyMRqawBtFOWEVUidQIGcfMbvgdHcjDZ+R9heUioMdWG/xX3dsx7A6WGgJuHbFX4urKwVLhZ+txKQ/2U7W9rHU6KyPNyMRChubaDXWVV0W0YhVVY26Z28P7A1/PvjhFtz/GoNCnhFL8XSx3twtqQSdiVUcRxTLqIVlAj0piV102S2NRqNZuSilOPdnf4C3qRuhQAmFsKwZfUT/+A1/xZ9+6F7ac1s4XqGnml5AW6KOVZNpYlVmFijF+PFjfPyOUHDNihivevoAe1bvnou3NSUqn60RR4PhgVkLrAIYiYXFTrNFja1yJBN3TCxtEbBkEZaNobySwCoUJQ/W7CSBtaGpAUsWsAOPLcM2ILhm2GI0561IgVWj0SwuWmDVaBYAYUeIe2EhgXh1xitdffDF163l82+6HWDBxbLLYTaiaj2MZBJLZnGCDJAoRUxMDG4bs6MXlbZTKdbM6vUbm7BkWfDO59KopSywAtlTZ/AqJiy37fkp3vKGWxdlcCh0kasVh0IRyABLCQqmzdmW23hMNfJcK0RyP6a3b/+szuE0t6AKgu1D7fQ3hlsxoLMhMuXfdFdnF6axr/isImJVKX6p6808fvAZfBWmbZpK4AX6WtNoNJoqfJ+WcRfXLIuiwrSITOPBCmBt2MRYPAucJBBXowjFx00Dp2mK1RlfGCYm5YGsUpLs0BC+IZBCYWFiR+ZfmJTZSy9OuuW5MY6tiZK1Y6VtE4W9AqEwlaAzefGFVjULiGVVWQgZCgpegJKStB/WKgBACBo7OwmEIuFmGY+E8dXCMEhduAAtVy9C5zUazUpGC6wazQIgIlFibjgYfLqjPJCb4C9/6iPsWNu88B1bJMzmNt70lMtnr3seeEFpQD/B5r7D8/r6IhLFVvnS8+vPB/TL85DvhT1d8/ral8p4vLHquWlZJKZIuZ5vaqZOCu3ButxRhJ6nCE62bMC1ytfbeGIHBkdnPEUgFMIQSATnmtZiVlwTnY2zW8C4vh8SRXs/ARiOg5QuiFAksCR4P/0zMFHgbYlYeGg0Gs1iogp5fIzqYk2WNWM2kNnUTJ+Z54XDBxiMRxCsYnWqn5t++I26i2JicgSrlBQeeRgrUAigYMHqnICTvXPzxqZA5WoFVlXI12lZ51ilKESTVb+rpFc9Dt3WvnKrzK8EwgjW6iKYeT+AwCfz8D5gIixZ0ZCIca7V5uqxPFQYWKQHh2H71Qvab41Gs/JZ0Nn5Jz7xCR566CE+9rGP0dHRUbfNk08+yYc//OFpz/Nrv/ZrvOxlL5uzfp04cYIvfOELHDp0iEKhwKZNm3jjG9/Inj17pj3u8ccf56tf/SonT57EMAx27drFm970JtauXTtnfdOsDEJBLyDq5YFolfAg/ICW5NKOnpxrjOYwD609fYyY2sxIRUGrF555kjVjp+b19YUToSNdwCsGdiQLClGAIDXG0329dFdkti0F+ab7mn3s3LSbTr88AVgdczAWy5xfWwSsSAwV+iGPxsoTEFOCo2BH37UzHh+I8Dp4tmMLOTtSEkoBrm6vLZ41QW9fL2mzfpEUEYliC8VE+RITgT+52rNGo9Fc4Si3QMGqiLpUgGHOaBEAcFql2CoEneOP8cqnfTKjQ5xa10hbvcamyZt3/jRfSDeXNo05gkjBZjS+kbFoG8+3nqNt4BjGPK68Kr86HcwVqr7A2t1N9+7e8Oc9XYzeNsonxsBt7yw1SboQGJAspChYUXZeOMyGtjoFvjRLBmFaGLL6GigUPJTvk7EiMGETIASJeISoFOTNfCmgw5CKnFoCdS80Gs2KY8EE1i9+8Ys89NBDM7Y7ejSMkjFNE8OoPyiYavulsH//fv70T/+UIAgnd0IIjhw5woc+9CHe/va3c8cdd9Q97l//9V958MEHS8+FEHz/+9/n4MGDvO9972PTpk1z1kfN8kdEowjDIOFlgGox1ch7tMSvLA8gYZqYboCpJHvOPcqPO3ZgBj5bT/6QyOgFVGJ+B7bCdrBk9UDcEHECpfjS36X5y5tNQDFkT2ESC9DdXf55nqPoktIkKVqqtjUlFi99zZhkEaDQFgErgbe9Fu7wBcPx5qrthgIVWUfeC6YtmCKLTqlPrd0CQK74tdacH6a5dXZlT0zDQkyMTLq6EE4EQwVMbLQk+MvMq1qj0WjmG+W5uGbluEBhGAJnFj6iP9oURz07iDIMfrpfEhOCPVd31W0rDJOIqB0bDTRsZyC5jcCAfVevZ23/GGuHL1ziu5kFQe2i3FQRrL3JsOhP+tQ+dhJ6hX99S4LVuXKbiDfOTx77JgC2FBiRKyvwYdlhWbRHGxgv+rHvXr2bguujXv1qMqt3QIVPezIWwZKCpFe+PgSCbMGbfNblTXFeEvuJgFxUj5M0msViQQTWr3zlK3z2s5+dVdsJgfU3fuM3eMlLXjKf3aKvr4+PfOQjBEHApk2beMc73sHGjRt5+umn+ehHP8qnPvUpdu7cWRNtu2/fvpK4esstt/DmN7+ZZDLJf/3Xf/FP//RP/PVf/zUf+tCHsCztwKAJMeKhYBh3MwjaUECueHl0jl2Y0SNrJeKmUrzlQCOCUZqzj5EZvIAKggWJhRSGwXBDUPoMFNBGEw1uA/GEzcty4cD6gu+hlKpKk1NuAUyLO67ZV9o2n/JqMDZKd7qV59raSVaMBZuTsakPmmeMyaUllNIWASsAQ4FrtXCquYWYD0ZxDm0gUAgGMy7rm6e+7pRQfHlbgnykus3WoaMYiakjWGtIlhdYjFgcKQtQ9BV0pIGnBVbNCqf7XeUFtZ6PjixiTzTLBeV75K2KolQKopYxKz/7rs4ugiOP8PQqyZ17BV3X3ja1x75lYQGmUATFCEABDCS3sWUYlIAfdcDDr9rIWP7EvI2PVBD6dbcVnQJGEgKZn51FwHqV4GzzDVUCqxOksYuarUAhYtoiYCkjLJvXbb2TH+TLn1PB9bjnuoO8dOw6YhWWD/GozWMtOX7iWKHqHNn87ATW7gfKARWXWntioeje3UvKViD9mRtrNJp5YV7z7AqFAh/96Ef5p3/6J1pbW2dsr5Ti+PHjAGzevHk+uwbAZz7zGQqFAk1NTbznPe/h6quvxjAMdu/ezS/90i9RKBT4l3/5l6pjXNfl05/+NAC7d+/m13/912lpacG2bV7zmtdw++23c+rUKb71rW/Ne/81yweRSGILm6Mt58JoPxGKeoHn8oL22UV2rTT8XBYxOII9kiY/OIAKAn75bvjl18J97+6a99f/o+7/RWCEaWGmBMNyaI2uJW5HeMVxxSuOK37+WYug72zpmP/59k7e/Sub+d23b2S97KRFNmHMs7Do3fNaDAUbUk3E/TCVLSkt1rY1znzwPFF3wqYjWJcf3d0wNhY+CNPvs05nTbOJdDrXnyaimzCC9e7jScyKZkIpNgyfQsyQeRLzwkfNayeSCFWeKESU0AKrRqO5Iul+oJv9O1rYv6OlOosGUJ5H1ikvbimlSDgXF+hx/QVB14A5rYgkLBshICqmv+fL6DzP4y4igjVQQfgIArZkI3znBbU2c1FvrOq5iC7eIrZmFlgWzqRI6vxf/hVmKo+sGKMKpYjaJkcSBSJ+niNthI9WRdZdmSKkJSE6udiHRqNZMOZVYP385z/Po48+ytatW/mTP/mTGdufOXOGbDZLU1PTlB6tc8XY2BhPPPEEAK9//etpaKiOrrn55ptpaWlh//79uG7ZSO6pp55iaGgIgJ/7uZ+rERpe+9rXApTOrdFAKEh97hVracyfY83YQbYNhaLFNqeB677wwGJ3b9EwM3nkeIrfvAuydih2LhSRRBxL5hHFNe6kC02FWmuCYGQQAJnNcN2oiQ9sHb4Ro+n1NDT9N15g3Yx0CzXHzRVu/xmksPGN6olS66qZF63mi3r6qo5gXd58/CtwXS5OzE+EkauTPk8DgRtML7AqATk7jP6eOEdDIYUv6/urzgYjnsSoEFhtifZg1Wg0msn4Pjm7LAoGBiQis1+MMl92G+bLbqPn72eI6jNNEILIVAJrcbMUivR8ZmdJyTe2GgzFoS8Jrgl/1vPHte16e8MFYKV45DM2L823cbaxk1xxzBkYIJA0507xlW0UHwLhRGrPpVkyCCGImoLvnf5e6ZEZGsGzImEgy8QwwTCJWCZZS2GnRipPQNYNUCssOGDtqM87+jp5x/lO7hy5MgN4NJrFZl5z2IUQ3HvvvbzhDW/ANGe+yR45cgSAHTt2zGe3ADh8+HDpS7WeFYFpmlx//fU88sgjHDp0iN27w6o3hw4dAmD16tV1o2zXr1/PqlWrOHToEPl8nmhUe/hoQk62mOwAOtJHuOv4EfLCYXfj1TNGdq1kSsOaZBJhpREiQCxQdJoRTxBTeXJGEggFnLzdyEg0y7nGdZxvXEtz3mPoD/+Cnxg4g/+pT2BIGI1v47y/rnSe8cRODnz7u+x55cvnp5++xDcjpd9VYIClFIn44g3+hSEwRIWoqpT2YF3mOI1N7HTjHDYTVdt394WLQS8eNyjMEMEKkLWro37szBh+YfoFiK7OLszG3vBJshzB3rO3B3H8MEIFJIvrnJYX6AhWzYonnStG081i7DzBckpj1VwCLS08+Y5RUlmQRu0qp/I9vFhjaQW07eWvJz7LCNaLuV6EEAjLLvqwVl+fE4UNBWAqAdb8BcuoOhGs1hS3qGzRD9yJxHCtBGJSv7OFH+MEmdJzV8gremy+XEg41Z9jzonyAr/6mjOkxDbDvwm7kKna5/kBXqBwrJUR7ekLSVe+gfPF631nJo4/eAGrfX6D1jQaTTXzKrDee++9F+VDOiGw5nI5PvjBD3Ly5Emy2SwtLS3s2rWL17zmNaxfv35O+tbX1wdAW1sb7e3tddtcddVVAJw9e7YksE4ct23btinPvXHjRgYGBujv7y+dYzZMRMZOR3Nz86zEas3S41STwXrDp1UWi7WkhhGNuhjaBH/wB7fQxMJNDI14ElumyVH+++9rfAE917yAeDFgbjABI4aFSJ/lhv/+S6hWSEW3wHi4f9sQIOD7X3iIF7zsZiLxuU8pE4HkdUfiPHhdeVs8m8Va1MG/mOzCqlnGmArseOhj5lplgbXyM1aWietPH4kaektHSLqQdsDLZTHHZ76vTYeIRDCUz0SxX1MJXeRKs7Lp7oatxQWrOiLSdPT29YaneKBbi6wrjO43psnaExZTtQuaynPJ2lGSQfj9KAyjRoCaMyy7xiJg14Xqe4aBAHvN/Lw+lDwm3/Q0fH0LgEKuqt90oquRSAzXaiBStFvKW+F964EvPMOf/aTJPUcMUIotfZeedaFZOBqs6nFwYJsIs30iZgKAqFcoZZtGChlydvE6tUwIArKuj2NNKhq7gEVs5xL31HHoANdswLXacPxh3vbPP80//OZ3FrtrGs0VxbwKrBdb5GlCYD1w4ACxWIwNGzZgmibPP/883/rWt3j44Yf5lV/5FW699dbL7lsqlQJgzZqpb/6NjaHH4cDAQGlbOp2e8bimpqbScRcjsL7jHe+Ysc39999PW1vbrM+pWToEpuDbyWGuzUZ58jrJOcfjW59cPjfuuSYXNZFGOIjtOdAFH13Y34WIxbGD8O9ZGqEgFO4I/3u6A64Zhrjv8/2tL2DtsX2k16wiEBGa8mGF9IkUpEKuwMFjZ7jh+q1z3k9liFLa9QQNqfScv85FIQQCRXk6pYtcLWeSxUWfQFj4RvlaO9wGXf3FJ4kIhRkr7ipysWQxgkrxzc2QWZ+jqatr1oVOJn8XiEgUQbn4nYXAE1pg1Wg0VxZRafCa4RZaVkUpFHIoVPVCp++RqcwgMMyL9mCdLcKyiIjy/eB40bHo+j74UScEouhB52wikAqzTsTt5TIRwSoRDCa2ULCaaJqhTKoMAgpWkqay8xuNg6dwPJ9HoiO8dsRGui52fvE87jWzx7YtDOUjRXEM41hkIu2szpeLdDYmy2MaK/AxlIcSxRDPR/dx51O/Ce4xjl2VZOR36xcUnFi4WuoEjsVobDXnm14UjsOUotm9sNjd0miuOJZMmfvx8XHOnTsHhJ6o99xzD7FYOFDI5XI88MADfPvb3+bjH/84O3bsmDLqdLZ4XjgwSCQSU7ZJFisZj4yUv3Anjksma70aJ5g4Z+VxGg1AXgR85PNhikpjvBk+ubj9WUySThLTWDyh0IgncIoC60T62K4L5UiHLcPFdsVB2pc338LV46McWFv2ij1WnFSY0qft3Bgv3KVmVbH3YlCGqCpcYeRdEoXcNEcsAGJSBKtCWwQsYxIqHApknOkX73IDF2Db6rr7lApjqnJ2tJjW7KNQGEEOmHmyWmkLUIlwomGRq+IFZyhwdeqmRqO5wtjkx4nlQrEoEo2RNX0SlK0h9pz12W7vKh9gGiQi8ySw2jYRUVQpDxyAOm5oSWnRJhoYzBRY3TAPdmlFgfV4+1b6G3cCMO40c2wgzZZV1XO0ZGDSGJgYpknBKtfcUIFPfDz02c8bkiBfv0iWZmli2DaWzOGa5c8064T6gDTCjJemt/xCaZ9SEscfI2+3I2WAGk9BchPkDk/9It3dBD8RWrbkokt3cVf5PtI0GEheWy6UIATn0s0oNfdzE41GMzVLRmCNRCK8973vJQiCUjr+BLFYjLe//e2cOXOGI0eO8J//+Z+85S1vuazXM4oTtEhkah/DiQjcyiJXE8c5jlP3mKmOmw3333//jG2am5sv6pyapccdvxj+/8O/vbIF+D2dXTBRsHwRUnBEPEHUH8SSOSBUWJ/uCEVWRRi9t20IDnSGYmvMh6F4M0k31HqS7kSqXhitMZbOkir4NEbtOe2nMgTZeHnwKDyfuLvIAitoi4AVQiBUqdplwZ5eCM3nprnuZACnT5HbsR18HxyQACp7Wf0Tts2rj3r8ZzE4XBoC11gyQxeNRqNZEDa7caSncIoFBP0L51BSlqLrOoYaudq8odRemBbJebIIEJZDVKRKz3f3lfelnHAcBXCDn6DvxPOs3r19zvuggoC9hxw+vX0dEwk1hoIfnR2tEli9qMXeC6swlUAIyNrJUvZRUCjQaEro6gIemfM+auYZy8KUOXZeCC2OCMA3quf1TWvLGac5QxLzBsjb7aHNhgCslmlfQh3o5YbdSZoCiwPy8sYz84lMj5N2YuECQkWCmU2MdMGnYY7nJhqNZmqWzCwlEomwc+fOKfcLIbjjjjs4cuQIBw4cuOzXmxBIp7MxmFjtqRRKL+a4wgyFPSajU/81moXDiCcxkLSO7UOK7pr92y58nZi6DQijRyciWSeExQw5YpQjS5XrMpL15kVgTTnxig3QUFjcQZ4QguoYQl3kajljq/DT9IzqKKPJIrqrppbVlR+anuXs8uRGCgVy5sWA6bwihW0TcwtlCw8UeVNPFDSayfT29ZJ2F9k+RjMvqKBsKtmahbwVwfUlKl8eC1gqXn2QZc1bBCu2jTOR7pOuvubiXnm8BND3wx9w/Qs2IeypA1MuCRkgEYzEqgWygfHqe47bECVa4WXsV0Q7KqVIFNIQi5GLmKhK807NkkdYNqbyONYSXovbB8NxS3bisg98mlubS+3f8CbBvf2nCYywmPa4FQA2iKmvzXxTjBemQQm4uhDlnvtv48F3LD0xXqbHOR1tKxaYgwBAQEQKhrOeFlg1mgVkyQiss6G1NczH7e/vn6HlzEyk+A8PD0/ZJpMJU7lVhXAwkf4/m+M0mkp69vaQ+pXwBtfgJOFvF7lDi80iG8eLaCiO/o8nR7CChzl82wtxrUaac8P81MF9/Osun+v7fsTBjps40ga7+8NJw+4+QAjywSEi1kbydjEdqZBnNOcxe9flmVGBD0KQilRYmShFYy419UELgRAIoShVHlJoD9ZljFWUUj2zvGAggN19WYyJSbsAdzoP1sDHNSy8ovipRLEMyywE1uk7Z+P41dkgOdOGffvCJ93di/5dotEsBb70d2l+4xVFIWnj4vZFM7fIooiZtxr5vbteXtr+e2fLRQQt4lBhXxZxLGxzfuxUhGUR/fcvcGDrFoJVPluHypk9QgWAyZE2sAh45tSPuO3sS4lefc3cdkJKcnakaiEw7aZByirfVz9qU5SbSDvJUoSjAr6xWdFz9wZe/Utfg1/RAtRyQ1gWhvKYynrXz+doiJaljpTwsGQWlAIhkAKilsNgQxNj+YGa4xWK5yv0e0PB1qGlWQDtHX/8El7V9NKaX4WhBOOZPLTG6x6n0WjmniUlsEopUUphmvVTWsbHw9Ldc+EjMuHheuHC1ObPo6OjAESj5aie2Rw3NjZWc5xGA9Bw4y2L3QVNEWGauIGLUNCeHWbDyDdRAm47AXYx+mL9+FlWjz/Duabrqo6NJWMYO2KII2kCI/xO2P/841yfugFomrM+qkIBiSDtJCgmXCOUonHzXMq4l8BkD1ZUOGDVLEusolA+FI8iBBxpD7c1FFJkKqKnC+7UAqvyfXLxhqL/KjQE4Tli3kyFsaZHCIHj5aqe54aGy9XVe3sv6/wajUYzF0x4ocL0UfmXgkyH858LDdVjkSdODrPl+VB8bXctyJejSRPzGLEmLJtIUN8GbVXqELATU8E1wwqGczzy+3/KXZ+Z26gCJSXZYlGvXPGtShmgpCRd8GmK2chC6KnanhUoBL3rbizOIRW+kAiZBaF9V5ctpkUQ5JhIqYr5YfQmgJJBGJBQ8XdQEAopAm46mydvxTAkrHEd9qTWYYvy30737l4A4oHBRx6LMTH+BnD7zs73u7ok4tJkINGKqcKFjrEoTIzUx9NZoHVxO6jRXEEsmUoRn/vc53jrW9/KY489NmWbZ555BoA1a9ZM2Wa2bN68GQijYaeKOD169ChAVUGtieNOnDgx5bnrHafRAGGk1cRDsyT4+hYYjkNrZaCdVHwrPsiG02k6U4dZM364lPIW8wq84p7bePDt38BW5QHZeH6M4ZHxOe2b8lyydpRAGCTd0LDfkYJGb7E9WOsUuVqsrmgum4kI1sCIVW1vyVX7RGfdYGorCN/HtSrS7JJJ3jq0joePv+SyxYaIV22341oOgVgywxeNZm7p7SXmh2LBbOl+oJtABlUZV5oFpLsb9veGj3lApsdRQCpaPf8Zz+SJFixieR9LVX9/J6NT15i4bCybaOARyICsHRYUmij+2ZI9SVt2sKr5ieSqub82ZUDWrhPIIgNS+XBhT+XKFgr9DdcxGmtm94CJKUPvfCMYQNf+Wb4Iy8aQ5S/KSmsKlEIoSbzoQ9zV2YVpmuQMRWzSGNo34rw0XetBnyxe1LYEz2ggZ3eillZsWolo3mQ4FkOKsD5EJePpxZ4zaDRXFktmhhKJRMhms3zjG9+ou7+vr499xZTAG2+88bJfr6Ojg9WrVxMEAY8//njNfiklBw8eBGDTpk2l7bt27UIIwXPPPcf58+drjjt79ixDQ0M1x2k0mqXHxqbaPMpcahzn7ACDlkcwnuItPWOsGX+G1/34Ie48+l3uOfh1Vq9uQ5gWwigPWkzDZDQ1t4MY5bllewAhEE1NOOvXEXuo/vfkgiFqbx7ag3X5YikDBfhmDFOCKQGlaMsMVbWTvk/WrZ8epwKfQufaMEU1mUQAUTk3fnaOVx1hpIQgb8+jeKDRLFMMxZTpspp5Jp2u8SOdMzyPwIjUfLbKc1FWC3/5DYNrh1rLkf1AMjF/WXTCtolOEcFqKZctQ+V0a6UUUgjcYI6XYQNJzirfB3ZdgOv7FHzuc6QK4b1H5cMxmQIGk1urDr/5rMk/fWWwtACYvOkWfuveZn7r3mYYubKL0C4XJiwCSuMWQhsvQXjdxQoZjEkKet6QxL3qOgYFq5kG36zyOgZIyFCcNdVV9DfdyUDjzfQn72RobOl5Xb/meAMTX/9l9y5FIANS2YurCaPRaC6PJSOw3nHHHSQSCQ4fPsw///M/41WkFZ44cYI//uM/xvM82traeNWrXjUnr3n77bcD8OCDD5KbVB354YcfZmRkBNM06erqKm1vb29n586dKKX49Kc/XXPOf//3fwdgw4YNrFq1ak76qdFo5oe3ven/K/1891HoC1K4XoEDqyR/++Vwu5HNI4Ui4aVozw5gCYW1Zj0ArihHrAYyIJcvkPfmzp9JuS4ppyiwmia86MU0/to75sQm5bIQAlE101N6Ur+MsRC4ZgSFwc4LsPMC7O4XNBXGsSZEUtOEIGA0W39SjR96sFbiBHMjsNp+gaSrSLqQDEwQkHe0wKrRTPCR9+yjoaGFe8fXcGemjYinv5AXlN7eUNwMgnmJYlW+h2dE2VZc89oyDNeMAN/8Js1iFeba1ey7qo2UrUib4RhkTUti6hNeJsKyMVGYRrWlmxXAsOmx48JRAlEcFhT/zxTm1rtSyQDPtElW3JIEwMAAueI4TBYF1pHYVaScMGp1zArvS2YQcNXQudKxPXt76PnoCD0f1eLqssEserBCqRCmAo63wuPr4XPXlTNUe/b2kHSSuCY05MdLgmzMA9+6miCyBjkafvbp3Bjp3Bgql8UXJq3uLmJ+WIvhBf0mj/7q7y3ku5wR2dZKIRoWb9s2FH4/TKBQjGULOrtBo1lAlozA2tDQwK/92q9h2zZf/vKXedvb3sbv/d7v8Ru/8Ru8+93vZmBggPXr1/Oe97ynVGhqgh//+Mf8/M//PD//8z/Pj3/841m/5p133kkikWBgYIAPfOADPP/88+Tzeb797W/zyU9+EoCbb76Z5ubmquNe//rXA/D973+f+++/n5GREcbHx3nggQf4zne+A8Ddd999Gb8NjUazEDhbdpAxAr68DT57bUD/rTdw68ZbuP3q2xDNzRzf3EzTTS/jidgYIpAQSKLZALNzHQAfe/1flrxHFQo8j7H85XlOVqLcAmmnIu3PNKsM+xcPUZ1Wp4tcLWtsJcjZ1emlQkmM0WEaCimkASSTqMBnMFNfYFXBJIH1xTcS+e375sQORSiJKcuvq3yfXL3UUI3mCsWKxbAj4aJDm2+zeXhpFmLRXBrK93nZmSjxSWtWUig2qQ3krRi+OVG8KbwZd7QkJ59mzhBOhL50H5sHqjskpOSb8UGG/FEsWYyaU6BSY2TcuVlwKyElBbPsr3msNXx8bqtXElhVPsx+GE5sZnd/6E2ZcAEh+OnUEYyu3XPbJ82CImyLqKj/XecLBcFo1bauzi6EE6EpH26PFz1bTQn9iRvIDVZbW0SVyenG9RRMpyqI4Ey8BX+uI7IvgyBikXGmXlDxPZ+8t3T6q9GsdJbCTL3EDTfcwIc//GEefPBBfvSjH3Hy5EmSySRdXV285CUv4WUvexmWVdtlpVQp4vViVmgaGxt55zvfyZ//+Z9z9OhRfud3fqdqf0dHB295y1tqjrv++uv56Z/+af7t3/6Nnp4eeiZNIF/0ohdx2223zbofGo1mcTAaGvnP5ABJaZIyfBrsF5QEoT0Q+qoB56w8zvNhJEQk3syEumjF4zgyg2eGK8fKcxnNeqxumCPxx/PIV6TACcMk4dQvArig1ClypS0Cli+WMqoFVqVoGhwGr4Ajx8jZrSQBvv8EAw/9J3z2/ppzKN+vmuxiGEStObpWlULgEhgRtg34oBQF29GF1TQrltbAJqpMnrNml9ppNzSiAolQYXrotQNaYF0MelcrsNJ0P9A9t4WuAh/XrI7aFwpUOo1qauFE6+bixvA/x/donk+LAMehZaTAxpHTHFi9gac7wu2//vt/gPqL+/HzBewgBxT7LASZKexlLhkpcU27OB4L7wU5C1JRePdDf8B33/43qF99BzkJrtVcdejm4dM0ei6wBMZTmkvHtEIhv87HaJgWca+vZnvekLTkRqrGsFJAYETpPT3MLdvDbfd/WRFpFjy1Nby4t4xAwi0GVDgwnPXoaFgamTR+1K4SWE3DAsIFDYFABQHjBY/YUpg/aDRXAAsqsH7uc5+bsc369et55zvfeVHnve6662Z17nrs2bOHD37wgzzwwAOlIlpCCG666Sbe+ta30thYa3oN8LM/+7Ns3LiRz372syUv1kgkwqtf/Wp+5md+BsNYMsHBGo1mGhSKUTMciNRMiIpiq/lANyce7QVgT2dXabcRS2AGKVwzjBRRvs/wVCnUl9I3r0ChsnCQac6daHU51AismuWMheDpNbtKz41snsbUODIapdlPh8V20mkUMFBMQ6sh8CmMpcAOvcmEMHCsubkPimgUQ/rkrHAi5JngVf5daDQriEIywoeeDhc8As9DKTWjLYwQer1hMdkfG+OHaxQ5G6SY40hNwrGFO8V3Xs5p5siqpqptrdnRebUSEk4EUOy48GO+un0NUlicdjJsX9PCx78i2DwaFAXW5rD/DQ2kMzlgivvHJaBkEAqskzAUKFX+XQ3FW0o/54rNX3j6SUTn5jnri2ZxEJaFqfJ196lVLRittZ9xypJcNVqgPTNIJlJdjPrYYIabK79IDYPBRG3BaoUi6/qUFhAWmUAFZO141baqUXrgky74rJ67Pz+NRjMNSyqCdbG4+uqred/73sfw8DDDw8OsXr2ahoaZv4Ve8pKXcNNNN3H+/Hny+Tzr16/HcfSkT6NZTnz8KxWDkI/Xb9Oztwf21m4X8QTRYIw8a4EwgnWqFOpLQbkuebP8nSJMk6i9+AKrAIyKfCmldJGr5cwbDwke2lFOJxUKWnIp7vtf1/OThx1UHySL0UfjdpS8F9Rch8r3cSuuVQyDyBwJrCiFqSqsN4SgYNVOrDWalYCbjCAIr3fTtvFPn8TeOPuiqULBXT2noKUoLOmCPfNLd/f8qdvFLBpMjy/tamQ0ZhHNVwu4u3NNjDFatW31eHWBwjlHCECQdDN86BsP0dfYwfZ4My0NEU4BIpDcfDpH/8RtRaZJpbPTnPASkBL3dW+AJ39MIEZLm00lSgKrEjDc2F4Um8PPqC0zyFhMsvrfvgCtteKZZvkgbAdDeQgVUBnGursPfuben+eVL/1gzTGDTsBnd1nc8vwPeWbNS5hYBABI531Gcx6mCjPUonYbhYossgmBPpsfJe8vjZR7FQQEjlW1AKOkrGmTzs/9wo9Go6mPFlgraG1tpbW19aKOEUKwdu3aeeqRRqOZb3LRSxcsjVgc2y8XusLzGMvNoQer59ZEsM6ZaHU51LEI0PrqMqS3FzZBflLkA0qRzGdANGIaWQ43B2wdDq+7KDCYcVnfXO3ZGqawVlsEzNW1qhQY0iPmgSEBIaaM5tJoljvSNoHyfcQ9cXhagVXmyxFcEoPAsAEPJUKxVTP/CNPkpak4Q05Ab3KOhURCaVAatdFyB1YF0ARXnazevnXw+TnvQyVGSxsCxZe3AeRwjZPc9M9ngHBMpUSA41ZEFgYBhWz9SMNLRga4aqLKe3mzrQS+Cu9FCsVQrLnqsOai/6aITbrvaZYdRrIRAdgyD1R7kDbG6o8RVl31ArxnD5Pwsrzy8Ld5dMOrKFjheEYFPqeGs3ziu02s4k37wwABAABJREFUtmOciK0Ki6gJwVikfJEJQ5R8fhcbVciBAM+0OdoWflekOpuwh/oIjKJNSBCQKmiBVaNZKLTAqtFormiSzqUXghDxBAkKDBcr6arAJ5N38QKJbV6+uKRcF88oi1ZiLqMCL4c6FgHzOo+fiOKBOSmapKlAwWBDR+lpxoFCTLFr+BSwE89QRL1xKqM8htKFGoFVedUerMIwicyhB2tlBKsSaIFVsyKpV0dAFSuhT0UwGPoMDiRWsX/DS5DCYkP6JFtPPh4WZ9TMKwpFtL2dF2YMvBwk5NxnmajTp2i4dh1SwJgjONwW2hHs7lcwPMqB1bDzQlgRfdf5Q7SNzW/UshGJYo5mcIIECtifyCLM8vt+eq2J6efDAomAaZgUCnOX3QNhlJ6rajMZmgKLZ9KD+FKinn+e4V3b2H1eMVbUmppzo6Gvd0QXSlzuGA2hNYYVFJgssDbH66fv+6bgycgod6tmBIKA8/z/7L15nBxnfef/furqc2Z6DmlGki3rsmz5mpEx5pAMHu4rAXKxhAQDCWwSFpawu8Dm2M0mGxKHhPALSRzIhdkQEsKV4ITDgGzjAzDYI8u2bF2WJUsaSXP1TJ9V9dTz+6N6urt6ekYzmh5pZvS8X6+21d1V1dU93VVPfZ7P9/Mt2FsIAgm+x3MjU2xJJKAID19+Q7hS5bh8oOLBCoRksNVN286ToFCAQOGaDttHw7F450tfx8MPfJKCEX4mSkpyWmDVaC4YWmDVaDSXNB/8yED13wuV7oxkCivIc/1Jn4NdsPfwAxz29vHTO3+TnvTis5mU5+KZdYdp02qJcLtohCAS76Z0k6uVioXgVPs6IOywbAbQUZjCkT57bttD/tt38dtf/CsOdGUAGI2P8o///htY8f1ALbdY+V5DRIBo2WTAlV3bsFTt4sDEwJ2OCMjlWvIaGs2ywA+/59NHUyUEyp/7wlhOjAGwd30/Md8GAcc6N3Mqc4gNo2eWcm81g4N4B/cj1tbExZtyaR5t1faHhsL/Zxx8o+74Gjn/1j2spuiZehL74NFW7cGs2ONTfDORJ0BRbqv1q5ietJ7y3Wrzq0D49LZakJIBrhLQ30/w7H1MfxDxwCDjQckLcG2H4XTNqZr0YPPYBGvOFhC6V8aKR9g2p4wCnX4O6EIa4U/DVIL21OyTsCesEiPjpxm3bOL+COPGFkAxdOpR2v/sh2wDnsm045sGRkAkYgLAQFAqzq8B4VKjSgVkIoEUlWOQUgjTxHBHkWZ3+Jj0aw5WbVjQaJYcLbBqNJpLmsV0+hW2Q8n0WeMVgdDR9/IfjpC9+xfpyZ1d9OBF/t/fx+vph8APM88MA2c5CKyISAYrLHGDlfvvX8KNX7pIy+AVhTUUnGgOXU/2ZPXfwnGIu+OQ2ASA4fmooElGue9FIwJaGGdhKoFR72A1DVxbO1g1qw/lN4mYCc5Riuq5FKw4vtmOqWDas3pw7SYtsF4A/JiNIuoU7ii21jmshODG/Bom3SQPdOUAHzktsorQuQpw2XNDFMZHMOIXxp1ZMOT0LlTZuT90z47t7o8s63qtFViVlLiztNvcVLApuJJSrFZpsW8tvOioJHPiBNaknphbLUwaPu/cP87fPm9j5NvQkZr5G5ge7z//BzZK+Ujfw/Gz1edzbo5czmck2c09W15Ksu4razSMcQu5fCvfxnmjymXK9Ycb0wzNGLL2vpSU5OsmOB4dHgJg5wXaR43mUmM5XKlrNBrNimXUkaTKU5hB2OhHqICsnTj3ivPg45efRBogK0dqYVot68y+KAQzIwK0g3XFUcokedMhC9eMR/6eVww/Vf23sB0cv0DRDhs8xA0bgpllmWFecGOcRWtKZYUCM6iPCBB88UVdfPgtnbz2F5fB70GjaRHNBNZzOViV55ONZ2Y8PpxZ06rd0pyDrmJ4nCravUwkdmAfPtuaDedykMuhlCJf8mZ17F81Gk5+bTh2gFjpwmVDBkZ4Y+fAjOc25aOjhLLf2v3ygiAysbttDBI+tHmCjN1GoVCkqGrnh4QHiakpYpO5Jc400lxILAVbRo9iyyIFCwoWrC1nSafOPQ5XSrE2n0OoUKFUvk9gwI8veykHu8Nt5RzANBFm1JNWLC0TB6v0ceNJ0oFFOrDoeNnrEJaFUy+w+h5lL8CTAY8OD/FLt2b5pVuzdN7eeRH3XKNZvWgHq0aj0SyCT7/tC/zGp34PKcJaOMOymXBaM3o3yw3bMU1ss7lj44LSmMGqFMESX7AM/kLl4uzOwUW5jjU1ZMyi3OCIViiU4cLA88MHnBiGijYnaS9ZFFJEYiK8//O7yJ5+QIYOCsNs2WSAQGCoML9verIhRpwNMo50l8HvQaNpFV4osApCJ6pvAPIcAutvfIRJJ8zay1WM3Qe64ftrUtxxucW9S7e3mjpysQ30FV8ARTDklUyVPNriMyej5ovyPD7yM210BQ6vPRKvlgCnnTQTwQSogHqfzLaTT+D4y0P0AXD+5I8JvvbV6n3PlyilKuXWUQbvrJUtz+f8roIA/x8+B1fsDh/IzFwmnyvgxqKNrBJu2ICs2T5oViZjlsQOJFtG7mFr9kqEChiULmKOKpe0kyZZca4ato0tJ3GtDDC79n7ti5/P3iMPV+8XS61raLsofJ9yJJ7JwInHsWSBlBu+m70Hv8fNV19P/rVvYNuxHKmyIh/TvwGNZqnQAqtGo9EsArOzm5icoli5jpJCMe60pjzPMGLUVx4K01weGazMLH/QDtaVhzIEZashK1gp7LqLdGHbmEF4//pKtfFQMML3/f0oasuVjQYhwTCIt0xgBTOIikyy8nobvQQqCHSenmZVMO1gDYC7toMSirE9v8sf/Mxtc4pCRTtBsTKiv+4MHM3AZELxZCLDoJ6UWlKUEf5dRlM3sKYyFxWYFgfO5Hjexvk5xJoJjOXXvIz1XeFYIu+kkEpiCpP+3n7uPXoP15x+iGc6duKbCdpKw7z6+NAFNWYeWmNStMOJz2atQm07eokZSIlUCqsV4qYK8IRZdfSqjEJB5TegEG6OYr6AWzcW2z4GyXIRc5ZYAc3KZF97CUhgB0VuOPUYAF0dV865zp69A7A9/Pc3NrrYU1lcK1OL3WjACiTb7OjkRcldHgKrkj6e2VA9lIgRs0yuPePhmTYCifJc8laMdmBfb3itki9lZ9+wRqM5b7TAqtFoNIvAWr8Rxw8H+VLAoS74oZHlz7uGFtw0qx6lFAK7/gFs28JYFs6Lmfuw1Bd2UoSvsK+SHaVZHCoIlftGgTXmlaJ/XSlxpIsUtb9xXJkYxJF1AmupoXzOMFvnYAUiGawAvuGgCL+J/slj2JdtatlraTQXC+V5UCoj6hQrQxHmsJrNh+wK+MFlcSYSRASCjLRI2zcwNPzdJd3nSx4hON6RwjejJclPL0Bg5dEhAD7+pRx8JhRb/aMHoSs8hk7GornXrsjhGae58uw3UcIg7geoUmFx7+M8+PR/hK7anZ+aOdqx7YZJNynxpaIlp4UgwB8ZhY5KBqwClCLhhz+AkgWFqRyuE/2bdBdKtMnWRNdolgejto81MsGYmUKWJaWJCcyOq+a9/q+//Lf5r1/6KHmg/3T0uXRYOMPNR39ER8dLIs/5vo8vA6yLbXrw/Ug8EyKMZ8pk1pEYLeKZdliZVBFYN7i1RU1D/xY0mqVAC6wajUazCIx4HENGc9EEMXKl4uI27LmUTZPpCDEBdDrnX27YUoTAEHWSqlIEF9jButCSQk0U5YWj7LIVqwqqAoj5pRnLCRSmKgOhGGsCQsVB1NwPRSv63YzZZssmAwQCgmhDicCw8Yw4iDLlfY9ogVWzOmjW5IpQeG3MAKw+d/wY8soNM6a9YoHB9aVe7qPF3ds1EZQBSqyZ4X6zxfzPiUPpcAyRM0PBUKGQtgkoXnMYjmXSGIEKD77ASGIKgNceUggklx3P8VO31wSg5XBGtJ2G76tSeDIgbi9e1FEywDNq2zcRlUZjtT9CMVfAtaMTiMmtm6Fj0S+vWUaknTT25AQ/+90iV4xVJoK3z399q6c30hBqb19YBWAosKXHzz12F21nshR6uqMrSknRk7RdZIFVycaIAEHMNina0O0VmYy3gwL11a+SO3ocfB9qhxKNRrME6Jo6jUajWSRvfPgMbW6AWbmeKtuCZ9dsioiACyUolwhEVLRylonAKhozWAGdELDCcGsC6zQKMGW0DM7euAWgGhMAYChBl7qRf/t0jsEPdDL4gU6OTdZZP9Jp4naL52+DHIaKCkVjqU0AyLEWNZTRaC4y0xEBChhPbOK5zE3k7PXVbNam6xiC552K0z9M9Rw0TTww2Fa+bAn3WKOEYDSZmfF4cXJqXusP3jmIH/j4gV+N2pn+M44nNvLtbS/n8b6rI+vE13by9cw45UKe3MQY1tQiJ3SXAKuxyaFS+K0Kaw8kfsOEQ31M0XWnJIV/+Bwlqy6uSUHCd9Fo6jHXriPmT3DjScnRDNWoCSlg3fBR7GdPEcuVicVildzjECUlJT+YdbsXDN+nbEUzWOOWQcEWJL0CqMq0w5nTTEkITIOBfIodhQQLmAPSaDQLQDtYNRqNZpEI3+OG4QKPrA/rOnefshjMP59Xfuk/4Lbz26Yq5JGmXc1gVUotG4EVMTMkYEnHaVLyWG/4z7ybg8FBhnbdHz7Q1iz9TXMu6h2s9The1MFqrl3H6/b7HOkuc7gTEFC0FImgg6tOG9UMvG9ujdHlhd+CNJCIzd5g4nyQQpH0RoHe6mPD7dcQiACjo3v2FTWaFYTywkmEZzs3cSJzIwgoxbdw4HSWq9vam68kBEUnXuk6N/NIfFWpd+Y6mpahDMFIqhOr4aPPj47O2tSp+YbC8+rg1vvpkBYfm7iC41034owZJBpMyH/xEx/jhXf8PcVceLxuu/nWZVfJYVoWhpIEleZcKgjw5NwjhY//4VA1IoE9c7yfIMCvK29OSxMaJuCKpo0fcbAqUnJ55GZqWssj6wXpxqaw88Ts7kHhMRF7GriGbWOQ8EEoxaZnDyEqwr0Ri2EqFykqor2UlDzZondw/jQ6WIVhELdNco4gWc5zoDs8rtxYthhdt5ZH2m9ms7GeuHeWnrEnL96OazSrGC2wajQazSIRiRQbiiM8UtfqwUt1MNK97ry3GeRzmFYCpq8HlMK52FlPVRocrIoljwgoWWAqwbf+zofn7ocX+NE29poFERRCYTTi8GFmRIAwDMpjY6Tcs+SdNbXlAsFEupOnus7Q4VswFauJALkc8VgLhxd79nDiAxvpLEwwLbBOf9ue6bmeHxcsbm3dq2k0Fw1Vcdgd7NkWefzx4RxXb2u2BvimES0RbWCu5zSL55MDSYTZGZG2TQUyn6fsz6MkXilePNXGdYUU6U6fdDDF794DJzZuoL7QsN5ttjaTwhQmb/zF8P74/11e4ioAhoVQEioC63REQFMqGbTTE3bnQgUyEhHQn0szaUjqJxgKxSLSilVzNA1hkvjsZyATzWXVrB4OdkFgCG6aS5yHiHgvgGMJjxeeeQrH91HcAIHiGe97SO8ZDvQIbiqkEU4MM3CRRjhmUpWIgIuN8jxKTRysP8g9zZvdK8JlCJ32z/Vs4LmeDeRjkI+tY30xQGYnMDsyF2XfNZrVihZYNRqNZpEoFXD16AG+tnUT20chqOh+z6696rwHL6pU4BVHbB6oaLSBUtgtbBq0KITAiFxOqiWNCPATDr90OkM8MEilCyAnUQICoShWHK1VzjWw1gAQFMJM03oHqwDavDJpJ+oKnjJ8SuXHIX1NtXRO+JLRzm5+bgwSgUHZjlW7mKeTaeKx1rqtXUNhBs3LO/dNCq7LlelJx5o+r9GsGDwP37QIRHso0lXOJWdz5aZuSKUUpQYXetoNf8vTflbXWCaVD6sQhWIyuYmOMiS98DM/1AUokMcf4Ofdl59TYF0/GXB5PjzmWrbNFi+Jslye61hD/2kxI3/HNATdKYfxB3Yv0btqDcI0MZSPpCL+BAFeMHdJ9XteJ0knhoBz5MgGAb4RvYQd2tEGE2EsgxEoCoZTU6WFQLR3kHR08uRq5JXvMvnO3/ih8/Q81veuvZb95ScYGDvEy44c5LJDY/xy0aUtsGB3+DsTtoOp6sYg0qfkLYOIgMYMVhE6WE/EfVJudMJiOl92mtHUTuT4iBZYNZoWs0yu1jUajWbl0v/YGS7/tf9OOthffexQFzxwxTpe9CfPP69tqnIJLwgvltMudKZ7lpGDtUlEwBIqrKXuNuJB+N5j8SR+zCYRGFxdSLCurMWD80GVitDXR9mMijN3X6X44EcGqkL1dI5wWbh0Fk+Q8EJ3VtKHZzZfjm9v4mz6eYwlN4cuicp2kvHWuuZCgTW0cweNPwMpeXJ4fnmHGs1yRklJLpac+YSUlJvl/UkZxgPMga8F1qVhcBC1bx8lq23mcwJypal5OdzaDz0buZ+w43zqRTHGkp3IOoFwumKjww5z0Nmzp3a7GKTTtVszKgJrFRXgzxIRkCtm+dgXJkh4CrLZcztZgwCvoQO6KXJht3RUdZK7HvG615NoQYMtzerjrvfcw5e7R/ly6hTZs8M80eFysItQXK38xoQTqzT7DFk2DlbfjzhYhWESt0zKpiJWyM4YrBcbrHUjE9EGohqNZvFoB6tGo9EsEmGaiHiStHsCK7gSt675Qntw+XltU5XLeKZVK4N/xSuwl4vAKsSM6vylmsdXSiGTscgruJkU3/i2gzAMlBC4aYmj9IXTgpASjh2jvHNHtDGOKgMzxdGSkHS7E4wnNiAFdPo2z6zZxtm20GWX9qhZ5oB4izNYXUNF3CO5aVMUChVI9p/KcsvW7vnnHWo0y5FAMpnunDmJEEimyv4MN6TyvdDBaoaPtyXTCCYiy1hGagl3+NLmZ16Xw0mn6C6Cajj0mIq5BZhK5cXWdZKROHhGeJbzRYyMtRHpNz+n9S6TxIed+8fnfF6YJkbgU6jo+98//hCvCF7SfFkFTjLFTSWbMdPlSXvupl2qocmVADDKmNJHmpUIgPpJX6VwYjFiy6UKSLPsMNs7+PhnsqAUppqZZi0cJ9Lsk2UisDZtcmUbDPQNIOUQV58NeKpn9u/9ifEc6y/Abmo0lxJaYNVoNJoWYGY6MZXLtaeP8ej6LdXHLbFhYY0uKqhyKZIxJkwTx1wu4pGIlj+opXOwqkJ0dv2b2yAm4wijclGlFGVT4sxyMappjgoCFGFEQLKu74ekBMx0ZBVFQMKfnPF4woNiE4NcKtHacn1XKCxZjtTdXH+mchGU3QvX9JMtemSSy0R90GjOAyUlU7FQEN02Fop2gSFRQUCu7LNmOgajIs4pFEVr7t+aIbSDdamIKRvPCB3H28bArJtpNBTnbIKj6mScor2Wf712N8e6wyY7a4ZnLm8qyY70MihLng+miaFq79+AWTNYr3STOO3tbPRho58A4xzn8yYO1nff9MvcedfHKJpJZshjlkUqGdMTcJpZGegbwDTuR0q/6fPCibE2nkJWIpRUIMmXmy97IQk8LxoRUGlyBfDnv3w9z3tokqKdAZo3ox3JNY9e0mg054+eytNoNJoWENvRjytdEu6JyOMmnYwXFt65NnDLEYcGprV8IgJCZbPugaXLYA0mJ2Z97uvb4B+vhyPZozw6PLQ0O7BaCSQ5yyIQ0e9U1mheLnbaKhPzstX7/afhcCdNA88E0J5orahTNBUxPzvr88r3OasvFDQrnUAyGUtj1tkhTWGGDtbSzIt5JWZmsAL0D9fiZUQTR7qmNZzpysxwrk5jICi7cwswP3HdPsYScKbt+ZzuiGaqTkeuDPWFjn1DTTJ49GEysZUxmShMKxIRINTsAutWN0lXEcpmO9n4ZVwte1Bu6BYcvHOweqsi5YwMVtu2sGUtKmZv5XObrnbIZJpEOWg09dTFXTSK8cKJYdVFBKAUhWKZi43r+ZFxnKg0uQIo2IJMcQKAyXj4W5ANlxETTc4rGo1mcSyTq3WNRqNZ2VjrLuMfe0fpzo2QcotV/dFWgpH8eQzCfB/XiYeln6aJMIxl1uQqSrAYgXVwsHZrQE7NLqpB+DF3FQy2HsvB0NAiduISIwhmCDMuAcPWzCxTacCUKSHIRTP1CF1bCZ+qGiAApqZoi7W2QKZsBJjKp2/qVOTx6iWQ9CmcQ8zQaJY9UjLlhA5WaYQNE/3Aj5SjDt45yKPDQzx/x/3cdvV+TsVrR2PXhPaJscgmtcC6NMgn9nFtaW3kscbTYNmbe3L1cKLIy46s4bqzl2MFszfoefPB/+CVx77HhpQB1gpxJJsWhZQgMMKbUODPMlD474+lOJ1ex9O9r+DZrpvZd9krGX/mmerzQ8NDDA0PVUVW1aTJlW3b2LJWZSFFTaQ2fElnW5NsY42mwp7b9rCzb4CbzlrsPC3YeTY6kSEch7dc/Xr6e/vp7+0HoFC6+JO6pb/7TBj5JCtuccOoZg0XbMG2kQOR5VN1u6xQTJQkwVJ2qdVoLkGWydW6RqPRrHzOOj758bOkSxMgIO8InEAwfh4Cq5I+nll3IWWYy8jBKqIXgkotXUTAr/7KrOrtruOdjKdezz/s/Am+dOPNPJI+R2MMTRU1Q2BVHDWzIKIlrXtu24NlWPzZ3RbHzQJpdwIg0uG8kY7i1Dk7Zy8Uv/Ja/Scf4cqRI6RLz3G8bZJDXfBIYmLZNJzQaBaDkpLJWDQzVRD+Xps1uXICQcmqNbmSAvLBaKThnMDEP0f3ds3CKXUk8I2oaHewOzzv5x1B2klTLs8hsA4NEfcVJ9t3AFFx1gwg74S3pHuKu3f18s1dfXDbbRixuZuaLRdEJSJguvGUoLmDVU5OoAzBvr6rqo/5RoKHnxmZfeNNIgJs24w4WOs/T+m5Oj5mlTLQN0DaSSMNKMUtivElcnhbNkkjOhYtlt2LemxVSlEy7TBvuDIGtywLyzTYc9se/v2Xvku7X2DzyFe45cg9mEE021ggkL5kUrtYNZqWskyu1jUajWbl893PwDWnAzZM1cqsBZDPFxa+Md+PZLBiGMunyRVRp41ikQ7WOVAARvhqnpHmVHs/o8nNPNl7HfdsHcQ3YwTC4Ni6qxnu2LA0O7EakT4lOxRYcw5M4lEymjcWSTtp0k6aU7ZLZ/7orJs80AMHuuHKn//Jlu/uc4lQqIjLMjcfH6J36oeky6fZPgpXjgFf+QpFT4tImhWOlLhmE4diEFDyaxMIOTeHVBLfLUcanEhDkKqUhE5jKnHOLFDNwlBK4SfsWkOlCiYC0zAxK+Jf2Z1dYH00nSMe2IykumrbrXt+39rwRrCf4dwww7kwlFXEo6+5bDFNpKydUwrlXNNJAjlyhpLpcLSzq1rSLwPJwdHi7OJVMDMiwLFtkviYhkmDDoaQAZ3JFeL81SyYgb4BxKz+79YghCDlNFTmSEnRvYDH1sZqLykpj9dVeUlJvG4fhWGw5yUb8UzoLI5xw8lv87JDD0Q2qaTPWP7iO3E1mtWEbnKl0Wg0LSJRkhzugJJZJO/UBnu53CwdcetL4vfsiTwVOlijA6Xl0uRKIBAiegWjmsbnLx5VeR3XXM/pjheS8OGHG8EIwky3acxYjGO925dkH1YlDQ5WhQJVarroBz8yAIDjKzLfOkTcvxoIs8qMAMqmy7VnQ5HnVc8+ybWvf03Ld/eqDf2c2v8YUNtnq66jr28E2sGqWfFIKasTa4c6/epM1s1SzhCnlFKYQUCxzsEaCEgWs9R7+QXglj2IaYGpVahiOIma8hKMzHHqc88RW2IYtczH+oqA687A3l7FhLuPeDAJdU1sxApxsGKarEt2MRnU3pjnzfw8gvwUp9PdM9f33Gp+/b99Osf7X+mTPXovncOdnP65H0bHR4AdczBVGRl4zAhbCBSZFueCay49nFiMB4/dhxImaSfNTVdspegFtF2kn6TyPUpW1Jkdb3qcD/ObTOWxPjtM0j1NwekNn5KSkXyZLT2pJutpNJrzQQusGo1G02JSpUJVcJSBPK8gfOUtYwdrkwzWxSQEDPYPVf+9p+E5BUhhcKb9Zq4/A89kZtslg5H128jlC6RTOmvtXExHBKTcsAw1tCE3F1incS3BiFXm/Q99j2c6t7MhG+eqs6eYtI7zpifCb/wV5ThW95ol2ec33zdG0L0WCN1hZhC6Lg50gWCCO/71/bzu2i8syWtrNBcCz28+SfC9Z+/jZ67ZAYODfPxQ2OnaVGHGtxk4BJX8vVu3vIxY+TOVrGSLoOL+L5VdQB8XW0WQDyXskhX9TONellyi5kg9VwbrFjcqLNrS4+rTP2D72fU8kD6OX34KJ3k5fem+6jLCXhlCYRgREBVU3UaBdXAQZfqM2KHQXClUoJQwUZ7HWN7FDBSJRIrXTjkct4scVUAgkaKuFFyF4pcny5gyTyDSkZdJFXPErJXRHExzkdm9O8zzHxiYYXz400f+HCV3II0EOTcXVgLNcsxeEhr7DPh+pIIB0yTRILA+udYkOVG7f9LP0lWYRBm95B1QgWSqoB2sGk0rWSZX6xqNRrN6iLsFLMPCMixMwyQ/SxC+QiEff4zgoQehs7PqaFVK4ctoZ1CEgbNsmlwxI4N1MSH5Q+lc9daIEoozqS6UmPneD3dF7wthcPiJg+e9H5cUgcRtcD782g9K7Nk7wJ7bGmXuGr/7b3k6SkVefXAvb3zyB1xz9viMwjxr3WVLsMMhk+NnsUayHI+VMZRH0gvzYA0Fnd4KKZ3VaGahWQk1gBGo2nN+RaRSYfl/uc6JLkwTxyviyOg5x10GzVhWE6oYxv74DREBr5lwqpEqAOU5HKyG7ZBUNdeYqSDhF0m7Z9gy+ijD6klcS+BKl8H7jjF43zG4807MrqWZwGo5hoVJ9P2Xm1QZBM8eZSzeweEuKNrhDcIqnjNTJT7+R3tJOUk6pcX15Tael42HGaxmvUdIYCfCv8XrDpyNNPIBxbq10WZkmtWHaZiR396iaCKuAnimwFQ1w4SS8oLErwzeOcijOzqZKmYhVxsnK9+jZDY4WJ2owHqox+TH6Tye67L70QluOCGJBUWKVngN8sDxB5gqzD25rtFoFoZ2sGo0Gk2LmA7XNxqcgOWyhy8DrDoHqpKSD294nHdmY3xjW4yCYfJv7ffyAwhz+Bqvsw1j+TS5apJ2tVQ9SBUwkuoi7YaNP2bjQMUI9PFv/xV33nzHEu3NKkIGUYc0CjPwqS/Bb4ZV8iiPj2OV4xiuT6zoATWl2/AkZu/6JdllCCcfrMkC38gU+LVTUSE3HugmJpqVjdukCRCEEwjNnFKmSBAIUa2YsAyTmFekbPmVpi/hOal0DielZmEEhQIK8IyawCqlT3c5H1nOnUN8udLoxDeiAm3CK1CywZ2a5M/vb2dn3wDvHDgMhN8LQwnMteta9j6WEmGamA0O1maCc2AKxpId4TqEUQn9pyS54fs59PCj3JRK4wmJqgw6NhUclAzwG5pcOTEbz1B0FceYSGymaENgQBAoOrzzyMHXXHo0EVXr8UwwAne6iCbMYL2I2e9q2sEqwh+HSKcjGawQ5nLfl5lkKhvGaonA4tXHPP6+nWqU2WTh3FV2g3fWIs3mmoTXaDRaYNVoNJqWUZ05DwJkUHdhJSUFT9L+ipdXH/Lv/DvWuw5TsXae6t2FZybYIA+ETlDfp/jPX4S2Sldd08QwzWXkYBUYEUlVoRaTEXAO8s78S1sn8ya5sk86pk9vc6Eam4SosDz1nOzejXfgXhgtIQErmeGJNQJLKlQQkBzNI8TSZgU/3QNlA45aWTrrdtlRDkqpJX99jWapmE2Qu+bIFPLvP4P/2D6UA4ZlYSgfYUTdWoZl0mXHiavoRX/5QjZiuQRQ7/1VSoFgOixHBQFuuURnOU9/b391OdeTsx6TpGMhRE1gFYAcPcULnh3milOKzhfeDMA31+R4byXGPZm0EcYyGQeci4rAOp2jLgOJ22SSIB9LUrJi9A/D3j7IxgWHOhXgc198nE2ptXQXTlVHHGtcs9bkqq67pm0aFI2ANbkzPJvx2XXMQgiB9TM/zxUvf94FeMOai0naSbOzb2BJX8MzBCIoh9VngUQFF8bBCrDtWI6Eq7j17T75WJZDt3dy9rbHoxEBQNyOHh/23LYH+/fsygQ6iFSaB29MV3K6VTXGrNEEotFozh99BarRaDQtYroZUP9JHyZ8VCUjTEmfgitpHxoiKBUJTAP5kl2I58PfPe8mSlZ4kZW0r2T/ySw7Mhb5iRykaxcPybiNsVyEo8b9UJHrnJaigJLlIICEP6N1BQPDPgXbAAwQYCuL/ScneP7mnqXZodVCEMzowmwFzctZI26F24AN4SB8+m9xf1eBvv0TCAWmXLphRShS1L5ok6KAUacjmcrEDxT2MmkGp9EsFNefpaRcCBSQV5JEbx8Ygp/KmWQT0XLxtpjF83pv4LiTitQZlJs0F9IsjvqJv+9cITmSgPfd+B54ptbUMggknlQ4VvSYpJQiMA0SXpKSBUrA0ViJr78sz1SgsAwL77fD427x9k4YDTuFm8f3XoB31hqEYSCITto1Cs4KxWgqM+s2bFcyFUtzJnU9T/VuBgGWOIVXKiF33wJ7K5/HLS/BMg0KRkDSK/KC43t5+LIb8EybG3qTbOhYIY3BNMsazwRT1eVPSHnBMlhVIHG729hdsjkqixwivLaYEREwn6xhozxtikehUFKSK/tkkroKSKNpBVpg1Wg0mhYxLUSVhn7IOz53B75ZcRf5krzrU07H+MtXJhDAqw9B3umhZGeqmWMgeOrQca4euJxC/eE5nSYdX14DH6NRY12MwCrnGqCq6gx9wZoZRdA2epj2ZJpH1m0INxX4jIznQAusc6KkJJu0CaqGBYUl5yfCmIYJddl6OTeHWDoDc90Lm1DZx6tG4dn2IvXt0g0FeVeSSWgXhmbloZSa4fDbNhb+/1DG55vOca7f2kObKvOjy25if996to9GJ53aEw7s2UPszn+m+PRD1cfLF7IRyyWAAvJWvPrZS6EIKJCM2UBNYA1LiOWM6hNVLoGAq0fiuJXzf9aUIMpYhsXujbsvxNtYcoSIfu+UCmYIzpNmLZamfxge2FI7vySVwbOdm/jx+jYKNgSGwDQ2c+TsVE1cJZx8s01ByQxQmGwbfZYrxp9DIeja+EFd1XAJ8MGPzJ0f3wrCiICwnF6huO/IHq7sv3ZJX3MavyNN0J5mrQSZiJHMjfOuL/4ir7CujCzX6GBtxvte/D7edd+3CQw7nIiTMhw7zVUs9uhQ7d+3nd970GguFfRViEaj0bQY4cSI4WMaJqZhoqRP7t3/mVLcwFSVHCQgF1vLlaPRdU+fGcN1XfJOInSKCgEDO0nHl1Pn4CYZrEsUEaCGhykZDv3DkKxzsE4Lg+uPP8G6yZORdcazM5tlaRoIZEOGncAkOGcGGQDpNFhWeBsYIO2kMSqNpi4kZeFGYg0sJSjM0VRGo1nWBJXsbSlDYc4Ohbzpn5WjDLxEmiNdWxhPrg/PJYQTT0ULiragtz106jkNM2BzNVvSLBwlFHkn/KxNBUoILFNixxys+tmmIBRYZ6xfCLNai3ZYvaIA34S4LUg76RlC0baxmti+ohAN37sgiOQMB4/vI29Hc2jr45XiykQZ7dXv+nQjo6PjxbDZz/RNCGzDQNgxEKEQhpBgSERi/hFDGs1cuKbgHUNlOkrQUYKpUvaCOVj9zmgczIuyaewAynUTFEI1d7CmnTRv/LUMb/y1DIyPI2IxLFWqjuSV9JsepzQrm87bO6s3zYVFO1g1Go2mxYhYHEvVNbqSPnnL5nhmHQfWXM3mbJKh9RNkEwkSlZ4YsnI9fP/Td/OagU0UrHjVFioMg5Qzj7KfC4UAUe8lVYqliPlXQQACzqYcylYYEVDPc+oYPdlhiu3p6ucHkJ3I4gcB1krJqrsYfPKT+N3bATjUBc8lHb796hv4yYu8WwuhbCgSfgkIJx9MBQWdNalZoSjPx1WzH7PimJSsGCOp7uYLmCY9baHoF2soSdcO1tZTsGvCnRQQUEDYNgmhmFLTwoVsekxS5RInYx5lKxRHBPChl/02Pz1wGd2p5VWtsiiEDPODKo3YlFKU/YB0RRMKbJMTbQlk5WtvGhamnATCz3b7KKBU6GzdGC6Tc3OcmGrICxcCyxS4lVm+0cqfxrcEazNdaDStwDMg5tUiAixPUrrATa6OdPZSjF3LmsCndHAfZcuG6qSFIG7PvFYY//B45L6wHcygHE5EQNXBOhc5VxsXNJr5oq8+NRqNpsWIWAwzKCEDGQbh+z5nYym+t+kmik4nZTPGqfZeSnb7jHXjPhwbL4QO1mkMg5SzjObDROsdrDlbkbNVpFMpQRB2aq7LmFLAlWeP8LxnHuGLf3U3zyt1kM5FrT1BucxkUTu25kIBss7BGnYhn58Is7NvgLabd9N28+75OV6XgDvugi/8syKtavtsIMiXzt0Nd94MDtZuGs1S43t4qnZkTbu1G0AqMJmMt1NwUgBsngjPF4e74HC34PDGNB3p8LzR6GB1L2Kn6xXNLMcABeSdBP3D0ObCmlQvm7vWIWyHR567n72n97L39F4IgqYON+V5lKxaLqhSAdx556wTqUagMAIVujVXEO994fswVO1c/NUnv8xP/fPPVe8/3eHPaGIZ80aabitRp6l6DZ+pZRphRv3WbZEcId/zEPYqEqw1F5WyJYj7tTFGt2dRvAD51kPDQwBMxBOMp3ZRsjIU7B4m2l7MlBmQsytmDOYXESBi8RlVdkVd5aDRtAwtsGo0Gk2LEbE40psKHRsoph7cw7+tMSjZNgo40B2WdW4dg46yoC2wsBBYpsW2Yzme/uvPcTrdU4sIMAzSseUjsAohGk4eanEZrLOgfB/fMAlE9NV2DD/Fdft/jKIE4+Pc8a4dmKpUzQ+478geJkteky1qaih8USewCkK30Xkw0DfA4S0ZDm/JwO6lyQ7cc9se/tf/2o2q142UIpGOk48J8rHwiXzBbb4BjWaZo3wPv27qqnESKzANCnYveSdNM0zl0ZEKRbvGMlHtYG0tCkWhbhI0ECBEOXSG1QmKKggo+zPFbeW5FK26SVQFZhAQs2Zelg30DXB4rcXhtVYYz7KSMExMfASCtAtiZASePFh72jLJxZLVKAwRBMS9M2ydnjNVVN2tc2FXJhQm4wb5XJbuqYDurM/1J/T3XtM6/umd/06sTmAtmAHlYplgiSKyprnyaDix8h9XvSjyuK1iM5adT5Mr4cQaquyaO+0jVKJr5u6ZoNFoQEcEaDQaTcsRThy7bvByqN2PlLCHzRoqd0yT/q27eOD4A1x3SpIoK8oqGwqrZjhQEoZBKraMIgIaM1gVSzPADHzKDR1SD1k5UkdPYHhutXFF2RJYMoc0QnHBChSTJT0bfy4Cw6yaVsO/3vm73D74kQGAJW8yUY8KApKDLyV9/OnqY/liaY41NJrli/J9vIbDaFdhgvs2Zarnj4lEB4GAfOWwWLTD8nSBoluewKzkTTqmjghoCUNDzR8XUKxzoIYCqwuWhUXd5J6UuE0FVo+CVSeOKEVSuquuGZOwLAxV+zwMBYpanry0Hawghln5iCzPx5ET2IEHzD933jZrKmy5VMA+VQi3l8gsav81mnpEIkXMq6uSMU1kqYjrB01L81tFe2AxluhgYvr7XHeYSEyfNJTCCSRmYwfaJgjHITM5RanSTE5JSUFnsGo0LUM7WDUajabFiFgcW4bhqv3DRMTV6880WV5A3BvlUEYx1As/7lPVkp9wgWUYEdDQNn4x8qoUCiWIuhMJB32uGb3I+vnHApKuGzYWqYi6f/2z/w8nKFSXKZSmmCprB+tcSASy4gy+/gz87Nk1fPU/fem8trXntj3V21IzLe1Pf/0SVpgNNn3LF7WDVbNC8T2+e/Q+pFDIyhe8Jz96jpWg/3SR1x44xG/u/hlEZVIu1iCwNhP5NOePAkp1AmlYAeAiTAuzTlBEzRYRUKZU52BVQOqD/7Xpa+25bQ87c2l25laYexXANCMCqx/4FP2aqHPXK8MO7NIIb8Z//lW+1nWMfWumyDlUhGxACIrO7MKR1ejaE5XbwEBL347m0kYIQc7IV7+vAKpUXPLs901unDPpnnMuF5fzG/cKJ4YlF+hgBZ7uVjzd3RDlpdFoZqAFVo1Go2kxwnGw1MystIIdRgMApNzwhpTc6BToLj4eWbZesDRMg+RyanJF48ljkREBCro8iw6/4T1KSdmKOlgdz+NwFxzsggM9FbEtliBOTVgTUjJZ1ALrbCilkE0agFnzcD4sNxonHgolLbBqVibK9whE7fvcf1rQVs5jyyLbR6MZlNO8+unv8wuPfp3BI4+R2H5N9XGnwU3lyQA/0CJrq/CFgWfZ1Rif0MHqgWlGIgIIVNOIADyPol0XEWBZJJfTJGqLEKaJEdS+uALACCdNg8kJXr/jreQdqrd0e4qRmI8tw4yAQIRCVmTCuQlORWDdc9seBIK9fYLH11sXLSNcs3oxbAdL1lysynPJL3F+abd0OJOKzzAyNJ4TFiSwBjWBVUmfQmOWbJP86ekoD41GMzer72yu0Wg0FxlhGAgLrMAl7TpcNQpDfeHFAgI6SgVe9Ox+zqS72TZ2isvecB1fds+SYIyiPbPjbcIxwwYOy4Umu3K+EQFq8FZu3dHOlW4KJeARtb/2nO/j+nXbVYq4N7OJkRFPsC6WYKSyY5YwmSy0sNnRaiMI8EWjwCrmVVoGXNSL1le+y+Tuv5OYhsktG3dzqGHiIV/yUEqtulJbzepH+T4Sk9olrMIKfBLeBFAT4w51hQ2uDAWXZU8BYJRc7E3bqsvEzIbft1K4foDlaF/FgpluLDU4WD32lc+MQHf4cH+xg+df97Pc9tL/jRg+FnGwKhXMEhHgkneSHKhsY7IvwyuW2SRqSzDMhogAASJ0/ga5KSaD2vdRKUVHOoETTxEER4Ft4SQ0TYcckSxIq25C4c2/0lH9d7R3umY1ciFjiQC2/t1XcD/3l7hW2GwQzyO/xA7WNmlScGKRH0LRgkSDJhp/5zvmtT0jnsAJyqE7HPjxse/zoiuvwZcBVuO5o4Ksq1qbbrql0Wiao0daGo1GswSUDYUdTFXHQwkPjACEH5B0x9g69iwvOvYIl02dxtlyFbc9qujOH45sQ1Y6pKeXMNvp/GhocqU4bwerFIrriuFAVSh4XjaBmr5wCkIH66GuUFRAUW0wEAgoxisZtfEEP3/NG6odUQEmc8VqhICmgSBAno12ahaA1cTVutxJJ6IOZ9/zcaV26mlWIL7PlYeyGEHlXCEVViDxxKnqhXVghJEeZgD39pzAHpnAHM1iD48h6n6/TmOzJNW82ZJm4aggoOTEwox004T+foRhErON0MFayTXceiwHP/wB5Y/98cxteC4TyfZqKXsgIJNcfd3uhWlhqNpk567j8Ks/isHgIEE+x5QyMQ0rvAmT9njobi2KUdpK0fFQdz5a5YNS1Ztt1fxCA30D1ZtG02qMZAq7LpJKuS6j+aWb0FdSElcGpbrM58jzlZs0IBGbX26x0daBIWvvoVjOgfTnzGFtL8Ma6dAR2OTcmRV6Go2mhnawajQazRJQMgIsmaNqcwEQ4AYeKS8qblkbN5MbH+Fn95l84paAgm0QGFAQijYJmcQyO1QLgYgUCinUeRYOeUb0oj8WCFSpiEilw4gA06mbtFfEvNDSYiJIV7ppC8ui3Y4KCrLiKkjHltlntwxQgUQaZp1PLsQyV57rMxGPdtFV0qfgSmLz6KR7Lgb7h6r/1oWmmqVG/ef34Pduxaz8IIVh4uTzvGMoz91Xho8VLECEk1H/99vfx54sNHX3WZaFofxa5MBspeqaczM94Tfd8Er6lBqia2KOhSEEgWlyZvIY2bYN+EH4hywbM0UPv+zy/Ss68CuTgIGATGL+TZ1WDKZJrK55om/AZDzGo8ce5ur8FFOBQfr5u8JFM110xC3GPzzOB/7gFt7xlcfwzBFKVozN48f55AsNRtv7m75MYySGRrNUiGSamD9O3lkHhJMlp7LzaK5ZV2q/kCog5YUO8LIV40A35GxIe9HeDtPE5ymwimQKoXyE8lGVc4Qqlyi4sjrJAbUxkPjMrXxpTzd/PBge9+7tKczYpkajqaGvPDUajWYJKBmKuDdVFa8EcKALjm7p5Q/+/i7syujIdD2svstIlAPk6ZO85uBZvrWtFzOoNX3qWm4XXkLMuKgPztMsGjQRZoNCDiOVRkkft+5C9uENgi/3+Xz0K5BOdkTWSSTiEUFB+T7ZoqcF1mYEASdjNaeCEAJDBcsrhmIW0k4a08hVxXU75mAGLtKofE+kT77s07kK3WCa1Y1CIQ2z2lEdFMPeOD2TOdbkz/J095rqssPpUX77/Vfxz+8drgqyEQwDQ9VlumoH6/mRy/HKX4QAmIxneZjw3FI2o8eXuFM5R5smBG5kwrFszjwHTZU8UrH26v2brnrD3ALrCm3WJEwTs+Jg7R+GkmXjtK/FMxVyYpzsd+6DijNPvPlnaK98BpNxgaEU91xxAoDvb4ANnZuYsB18Ufns685Xll377C50ybjm0sJIpkj4teaDyvcYzZVw/WBm5UA90xM0C/0t+x7vftzh766NgSJ6vG849re3z68RnjAM8oaPUCV8Mw2Bz7/t+yKvuvHD0QUr8SjrJwNkIlZ9wedPpVBSVpsqajSaKPrKU6PRaJaAkql4z4/OMLQ+vL99NByavOtX34/8xO0wbqEMg6SKVQcpSkraiieRRm9VXBUoNmZizV/kYiEEkbjORUQEKKHoywkGj4aXpMJwUcXK7LiUuHUXp9eOmbw3v43fevMQ7BwAas5CI5HAknlcKxRele8xWfLYUJddqKkQBEgjevq31MoUX4QTwwyKSMPhulMS9YUvkP1fH+Sy3jbd4ESz4vANi1KlErTr1W/Gf/KLFLOT3HBqLz/c8CIgRcrNc5n/KPvMPqB505FqcyEjPP6pSgarZmGU2+O8vtBGgOIBOxQblOfOcLDGKxN5YUl8rdHeo+kcTyWK3PmZl3PPO75TfTxXjoYnxh2b+FwuzJV6LDNNTBVtPDiRyDDRu43TJ05SFHbVISzicTorAutYIjrZJ4XiVd89xv4XPod0utjLXuq9rI6tL2c1FwYjlcaR2XDQWxH5lReON3vSs4zVOzshmw3/PS20zhPluahSmbIV56pRkKbg6c6AtAsgyFcORQYGa9Z0znu7HgpDlYBQlM3nxqLNuoaGyF0Z3veefhKovTc7EATZccyungW9F43mUkGfkTQajWYJ+Ie3fIED9/4s15w+wOf7twMwmfR5/8ZOhgArH5YUGdNOu3SaXx3M8eqpY5SsHUB4lX3tmUN0pJMX/g2cg6i+qs4773R6rbXTkU5Codzwgkz5PuX6C1kFsVm6pBrxBHaQx6XibPU9ssX5dVS91FDSJ6g0uSpYICyTnne/+yLv1fkhnDhOkMOlg0Odislun7/fYCLK9/Pwxd45jWYBSKHw6yc+LIs//vUb+T//5z+4cmSSa099i5c+GyceuFyeM/jQ5+5h8j/P4jpvaC6EUpT9pW3EstoIigVK3W0IwkiagVIbg5+5lUxJ8etm3cSdYdQ5WC2sumzDIJB40sVW0fzEsuvT31uTCBOrtNJCmCZmUMunnG7Ks3/NC3nqwaMRkSqdTtFREVizCYNiqQAkkULxcCLLewtrcM08RbsLWcpWm+6YSpBK6olUzQXCdviNwd/kC+NJCtNN2jyXnOvTwywCay7H4Nsro10rt6DIIeV5eKbNge7wtSZTBhM23HL4O6wpDTCeCBvjpkoT9LY3z2ltxu986Ou85zO/y/SvMykFEw1jZqHCsX6HrB2fAgykUkgtsGo0s7I6z+gajUZzkRHxOI6Em048zg82nMYz08j2GG1xm0Sp7kI3Fw2LL4sy67PfZeDEelJenoHxLCK+/C4eWhUR0Cy7VbmVIV8gcU2H/uHwrvG61xJ7xYvZs/4zM/cnkcQO8rVteB6TJX/GchogCPCNqFtqts6xyx1h29iy9hsyFGC2IwOfwTsHF1cumtONHDQXDk9Ef5PCsvjaW7/K0O842LkiLz+aYG2uCEB8olRdpimGgZJ5pJkJ7wcBxTkamGhm4p96jvBMF56j4j64D36PUmAxab6yupwwTJLOtIPVxBIBZlAC4hQsxVhM8fjxaIOmkucDtcnDuLNKI00MqxoRMM1Dl4WN2lKTE+FFaEVk3dDThqgr+y/kstwdn8IXCmWYGH6AUDmkCMcNhYqm3SZNEsllVuWjWbUIIfizx/6GngObkFYnphKoR/eRu/6/zbmerAyxiubCjsPK9yhate+3FJBKZoj5U7z08L38w8AmpOHw0ItNPrCAmCezsxvbn6waU5PS4MRE2BxWCIHnl/iZyV4MBGNpEwgYT17FSOpqBD7v+eKH+cz/+PKC3otGc6mw7AVWKWWYD7cCuxtrNJpLF+HE+Np2xTuGIO2exVAjHE5uAcA0TA51h4OsnVftDv+/f5yHgcJD93D4A79A7+SRcDt2HBGb/6z0hUDMaHIFzQtV50apmrx6vGMdn79hPUl3jJG73scd19yHkjLqYBUGsVkyroxUGz/xxAT/cm0oqn7v8HdZu/3KBe/TJUEQIBsEVnOFnmOF7eDIqep9EwFGxxxraDTLE+/kSdhwffW+ME3syu/SGZ3C6rYxlImRzWOVw9/vT/7l7ury9VMJwjSJISlP/84VFD0dEbAQVKlAmIVTO7f92bcshGFw9Ko6Qc80STmVz7kieIcu1vC8bQKmqFWhKKUoNsQ1xFe1gzUaERAKpA0oxYaeWibtntv20Dncyd1/lsU0THbm0pQ6JGaQQwFpN2wMBpAzJcnU8puE1qxeCo5gf1eeiUQnoCgYZ9lZnn1C/+feanKT205JSB4zFzhx67mU7DqBFcVPPu/nGH/kbzGArsIzeCIgH796QZs1OruIV7Jkdx0DgST7W/+bk5//C9anbYq9GcaT4Y+sbCjGEhnOpq8FAQqTnH8No3mX7tQqnRzSaBbBBT2j//Vf/zV33303f/7nf87atWtnXe7+++/nm9/8JkePHqVcLmMYBps3b+b1r389u3fvnnW98+Gzn/0sd91115zL3HHHHXR3d894/PHHH+erX/0qBw8eJAgCtm/fzlvf+la2bdvW0n3UaDQrD7MtFHlGE/DqQ7Dej9F25zcA2Hncq3UUbcxWCyRf36L4iafDu+v9MFR/WSEEETlOqfNzsPoeCMg7Xdy35UWMJ2E8eQXPVcRl5XvRZiKGMWt3eJFKk/CKXDUCT3eDGBnlEw/9JT+786PnsWOrGxVIAmFysHJaE5k0V5vLv8FVU2wbOwgvWMwADEOAkWIFzB9rNBE8q67JkRBgWdimIDAED60PSI2dZf1IpXogmZl7Y4YZOlgDiUJxz9E9fH7yy9y/7c+X8B2sLoJiccZjTjqN6TgU7JqgJwyDZEVgFbaNYzpYda5NQwnM+ixw36MUAA88EN5Pp0nc8sIleQ8XHdPEQGKoMvWOXYg26xEyYH1HVCQd6BvAbB8K72wbwPr8P2D9n1fTfxqOZmrLKRXQnVpek9Ca1c097kFu8WuTJqnAZCo383gBEBTyvMDtrjZ0TamFjU2U71Fy4tWqsXS8nXQmw4v2uXzzWoexBDzUlou4v+eDkW7HVGPY/iSPr61MbqxNsuPYMGu7TZRl4lY6Lt62V3GyfT0CSFXmS9yzYxw4M8WLNs/URzTLg4G+gYu9C5csF+wK5Ktf/Sp33333OZf7q7/6K7773e8C0N7ezqZNmzh16hSHDx/mz/7szzhy5Ahvf/vbW7ZfBw8eBMCyrFkPTs0e/853vsOnP/3pau6gEIJ9+/axf/9+PvShDzGwQjt+ajSa1mD2rueM6eKbDkrBsfIo19cLpbM0rTB7eqn3d5SlS3tiuWWwzjwmBueRwaq8MO9pMt7HmrqxaWBtDbfnuri2U81oE6Y5u4M13UZbXaMmBwOUjQwUprFCxcOloiEiQAHWCv2MpiMCEpU4iLgy2OQlSItd/NlL/nZR284tsJRPo1kMrh3tIm9YNqYhMKu/VX/GkXe2CIywyVUo8qVdKE5MwNRwa3d4laPKJXAcPDP8HJVQfOO60ElmGrHaBZRp1UUEWEhBJSKg8jQCy6id+5XnUv7Kv0G6N3wglyMej/7tVw2VBp6OPwlE8xr39oZRAQDdU1O0neMzMNeuQzJO3CtCnWDdVpgg4ehu5poLx4G0y6DMRx7LZSdnLDd45yBbRiU76h7r9WPIyQnM9sy8Xkt50YiAwBAkbJOxUpYH4yblICDvKx5eYBySEIJR2yfpPgfGNeQq8x9/9C9/yj+/5Z3k7TgTyR0oDCbiB5lIdUac5yaC01PlptvWaC51LojAetddd/H5z3/+nMt961vf4rvf/S6xWIx3vetd3HrrrQghcF2XT3/609x3333cdddd3HzzzVx99cKs8M2QUvLMM88AobDb3t5+jjVC9u/fz1//9V+jlOKGG27gl3/5l1mzZg0PPfQQd9xxB3fccQcf//jHSaWWmetMo9FcMIRhcG9bljePhg2g4u78BEj7iq1IFN3F8FI6NTq14JnpJUcIWqLHVboHl6zasXfbGGw+6/HA9m46OlJ4V76ptrxpEp8jIiDu1y5qUZDLnqHoSdKrtPzyvJESWZf3qARYKyQiYKBvgLQzxM7KzLywHd72SInvbMnzWG+K7aMw1GdRbLuOX/nUH3HHf/0dOtb1nddrPd3d5Dc77TyH2iRJs8c0mgWgpMSzLMzpOSLLxHZshBCknbDLsyxO1FY41yS+YUS6t0vpUxbnGZR9iaJKRSiVMQIAQdjPRqEQlM0YVuXjFIZRiwgAPEPxugNFTlZOa04gMOoEQeW6lK1oZmg8vjozREVFYLVlKLAWrahzdZqrnzvYdP0PfmQACCcSBNB22WYmDj+BFDdVl9k2cbS1O63RnIPr1w1Qfmq89oAQFHKV5naDgwz2D/HxL2TJ/QR05DsJRIzq4dcQBFPZBQisLqVEumo0CATVCYWDyVDgPHrF/LbVSPeG7fynrx3jb2/aAQikgLinePe/fomX7ng5U5VJj0c3bMBQBpky+AIQYCnB+FQts1Wj0dRY0qvOcrnMHXfcwYMPPkh3dzejo6OzLlsqlfinf/onAD74wQ+yc+fO6nOO4/Arv/IrDA0NMTk5yf33398SgfXZZ5/FdV3WrFkzb3EVwliBIAi4/PLL+dCHPoRTCaffvXs3w8PDfOELX+ArX/kKv/ALv7DofdRoNCsYAYGUtTvzwIgn+HZqlPdMJhGejzNWOPdKFxpBNINVqfNzsAbhZxMYVuTTEUIwlciQtKKfWehgnSUiIJ7A9soYKoBKgEGnZ1JwtcDaiApkJIN1pThYq269z9QETVEpq065ebaNparfSl+AxObXPvtbfO7Df3Ner2cGglRgkDfmyK4cHIShofDfunJFc54ot4xvWKS9yu8wmcaqOFp37g8v5H+0wWDeWdeGSVwEFferj4EAocuoF4IqFxlJdvL4+luBMDsUdZru/F46PQOmIxdNM+KgdA1FrDLZl3bhZ4cTJPKdqMFbEXvuIfipN1Nqv6K6vFCKhLNKz1GmhStdbH+CnANtXphpe7gLTOlRNg02jjzDjV0zM1SbubN//61/xyd+81ZQgvXZPtbkz3DZyOGlfx8aTQOuKlKsmK6FZZIvNndzxiVMxTbxTNc2zKBEt38YVcg3XbYpvkep3sEqIGGbpJ00lhHGI51vKfipNoOUV2DwmQm+sqOTtAfbRxUHyxMUKmMrAQTCwECETWnrhorlYpGSF2gHuUbTwJJaVv7lX/6FBx98kCuvvJI/+IM/mHPZkydPsmXLFl7ykpdExNVpLMviiivCAcn4+PiM58+HAwcOAHDllfNvhHLkyBEOHw5P5j/3cz9XFVenefWrX43jOPzgBz9oyT5qNJqVy7TzaKFMmj7OSBY7m8eUy9N11CjHnVcGq5QwPIxvRN07+9bC/741zWevahC2TIuY3fy0JYTgY1ePVS9sAdK+QcGdvfHAJYsMkMJk+5hg+5jg+Ze9YEUIrM0QdngOTrm1C5ZEmDyBUorR/Pk1P1G+z5vGu/iFkbX8l+M9vPb9XQx+oHPR+6vRNEO5ZTwjKrI5TrRk2mzv4PCWDIe3ZM7plA6bC9Uu+PO2YiRpc+tnXtayfV7tqFKJRzZcS96BvBM6L4tOL891vgoAqcIJQtsySdo1geEPX/PHpN2o2OIn2vBEeD5TIvxbSCPsLC4CRdxenQKFMMPv9FVnn+N4e5miqYhJOOg/yNmpO3nVvX/D9r3fnLfbx960jYOxAv9p31Ge/9wP2Hriaa7/0XNL9wY0mln4+acVCR+Sfjh28lyXkicZ7B/ix6ksxYRFv+pmquNKTmRu5KqxdrZNrKUz/2KeODUzTmA2ZkQEVATWD35kgPQLdpN+we5Zo2LOxb+8917KuRxXnQ2bPmwfhXQ5/P/0vPJcQ3tVKpEteef12poLw9DwEEPDQxd7Ny45lnTKVAjBW97yFt70pjdhmnMPHrZs2cJv/dZvzbnMtLAai7WmlGZaYN2xY8c5lqzx5JNPAqGr9nnPe96M59va2ti2bRtPPvkkJ0+eZP369S3ZV41Gs/LYs3eAR9x7ACrlhfPj0JYOYGIpdqk1NDa5gnkbqyKrBBJcl4Fhp+oEgDDbSZltCCPafdixLYw5SpFGbJ9YvgSEmbW2EhQ8naPZSKODFSEwV3CTK4BHe4t0ujOfjgWCyZJH+wIzDt1D++n1HDwDugKLK7MB+zplza2q0bQQVS7jm2Y1s1LceCO2HR2i71yIS6khIkAAKMVjp59a/M5eAqjBW/FHjnHq2tcBFffqLHQn7UiJrIgnSXjRhjclO0HWtlkD/I/1T9DHdcQrpybR3kF8lsqMFU/l+/y2fT6F/d9hOLORUXuS/dajnLIg8MPz/XwRhsGP4lkmRiZBKZK+QFir1P2rWdb8/U/3kRuplMcHEuV5TFay4F92KkZHRxcidj0nk9vYdTzMHAZAKB44PsWaiSKXZc49Aaw8F9e06T8b/pasXa8ibhnnLao2UspPce3ZJ2m/eQ2w9ZzL5+qGUqpUZLLk0deuqyOWI/cfux9ZqRQcvHOwZd8ZzblZ0rPSW97yFqwWnfhOnDjBc8+Fs5TXX399S7Y53eDq0KFD3HPPPQwPD+P7Pr29vdx000284Q1vIJ2OOtCGh8MmAVu2bJn1vW3cuFELrBqNBoD/9pZM+I+dAyzk1PZUpR/E1RMWba3eqUUjopdESqFQC89iCsK+qq7V0F0YAUYcQ8QriXfhNuPO3CLZU5cnuMIqVcVaOxDky9rBOoMgwBdmWO5FOBm6ch2sNpavKNiFpgKrKQOeHctz/frMgrbrHTsCgBQ2a0rPY3/vWvJiPxP+98lYtaZzg/1DfPxQWKY3s/ZGo5kf6j+9BV+q0NVvmmBa2I2THgvJ9zVNDOWCCkCFnewBDFZn1udS8DfXW1ice+5wTSp6LWCk22gvT2JLDwhPRumy4p+7ruVtr3ot3WuSBGZtitJQgvgslRkrHWHZOKaDoUqkvRLXnjnE39xo8EinoP+U4j1vFHTEOtjzifl/t4UQUIkkKsZXqTCtWfaUbXCCAoHdET7ge4wVwkHIzlIX397+SjZOpXFtIgYCgB8euZedYzfOU2D1KNePkU1j1qis80FYFk9lAjKFvVw7XOTJ3uuQ8zwcBeVSVVTWaDQ1llRgbZW4CvClL30JgEwmw65duxa9vYmJCU6fPg3AvffeSyaTYdOmTZRKJY4ePcqxY8e4++67+Z3f+R0uv/zy6nq5XHghtW7dulm33dERHmzPnDmzoH2aK6N2mkwmc043sEajWSbs2bMgUXXFIEQ0g7VCoGBBRkgp8Q0TKaKjOUMJYiKB2ZAZGDtHlmrBDHjRcyW+WilKCAJJoVCac51LkiCIfubCWDFNrhqZLkFNeGcQBChmfpeOn51csMAqP3Y7fZ3wWN8AJ9vXEwjYwfU8cfkEu04dqy2Yy/Ge14YXGOnk0Or8vWuWHIXidNIgZyvAp900F/WbFEboCwxdrLWMawPtNJoPat8+8pevZ1oXueE0ZErwwMaa4GpWGgX2pqOfqZFqIxCKvqkzwAae7oaB0+Apny8krsAQYXPdghU6i5Nv/flV62AV8Znft3VdG+mITSFEtvoZLoRH7gjovD2MaxnoW9jEtUbTCvbctgfv5HF+5eMfJEd4za88j9NTJdZNeBQS2zDk7BFhZgBj+eaZrTPwPFyzTqE1zFmjss6XIJA8msrxnpEDWIFk7/rrkYZB2g0jCaYrIAaG4d5afDQ/OHIv19zU39J90WhWAyuirmJoaIj7778fgLe97W0zck/Ph6eeCsukHMfhPe95D7t378aoDGbPnj3Ln/7pn3Lo0CE+8YlP8LGPfaz6nOeFWSOpVGrWbU8/NzExsaB9+tVf/dVzLnPHHXfQ3d29oO1qNJqVxUDfAKV4eMxru3n3Rd6bmQghaGZUDR2R81dYlfQpm82P59d4axAiF3ksfo5j//b1NxD3a4NWhaKQL86xxqVJs4iAlepgncYKSrz84MPceeNVXDWaYagvfDwQkJ2YWvD2BCCFRS5+OcmRWufrZ6+4kl0Hn6wt50t2Bu2s8R1GsXRHXc15oQT49RmspoW1mNiOikPSDFxMZRELBLYSCO1gnRfKEPhGDDsIM52n/xLpikverDiC436ZyzujLjSRSqNQtJePs3VsA4e7pp8wUIYiH7+8uh1QmLHYTLfyKkHE4nRl60oLAsVv3PKbPHjsjxhiCDi/Bj3n29RHo2kVRiJJzB8n52wAQPkev/XNP+IN5XaOdl9B7xiUpi3wqjaGkAIMBaNTpXmNF5TvRsbJwjCJWa0TWG/ZGF5jPJK6l/yZPOvOnCFz6km8ZDsFw+SJda+gXi4y67wVjoRs/vxMDIN31pqV6tJ1zWpj2QusExMT/OVf/iUAN910Ey996Utbst2rr76a//k//yeZTIbNmzdHnluzZg0f+chHeN/73sfx48d59NFHq3mr00LrXDmw085d121Sr6jRaDTz4HwbZF0oIsO7SrmeWmgOaxBU4wEUYcOPynUrk7EYUrSzrlAb0LWn5nZfKUOQcKOCav5cDtbB2iBvQSW4K5kgQNaLOatAYAW4YuIE/+u7J8jGuxnqq40VpiZzCxI+lQqnCop2J9c3FKKc7uxjKp2mrVLNss1P8etPViZchcA/eQx7wxVoNAtBofDqXErCtBZ1ES0Mkw/v/jDfKrTxjScfQgAD+TTHguV9XlkuhAJrHCqNXowAMsVJkmWPdfluEr4CJRkYfgq74XrASIQRIusnT5L08sD08QF8SzCeuKIqsKpAkXSsVTspI5yZ10pGPBSkp0XS8xFXtCCjudiIRIq4PxZ5TI4WONm+hrLVPmN5KyixaSKOEiAmcpSKJfKuJH2Oyizf9fDrJ8RbHBFQ3aww2TYiOZIJiI+NER8bw966js7cg5jBi5GGhRlAe/Ewk4laVuvEQiewp8fc/UOwc6Bl+6/RLCeWtcAqpeQTn/gEExMTrF27ll/7tV9r2bYzmQw7d86emNbe3s5NN93E9773PYaGhqoC67R7dq74g2kRtlyep/2/wh133HHOZTKZzIK2qdFoVh57btsDt13svZiDWS4GgwUqrEpKykZNVChaoeNw+xgc6LK4enRdpZOpAiHobj93XtW9u9YQuALTMLGAQmlhx+FLAimRwkBM/7kMA3MFC6xePs90YzMCRXt2Ivp8qUzZD+bdqVu5ZRDgmslqiXCuYiBRwI+2DzD48L0AXOOGCcl3bQ+f/f4ndvGlj+mu1pqFoQC33s1vmYsrG69EScWFisx87ch3oIIAsUIjQS4UyhBIIyoOtpen+L3v/ICck+R412V0FCdZX55A2A0Z4l1rsPxwQuc1T3+TH61/FYpQ2A5MA8+Mc7BSiFayLa6Prc54AAirXc5aHjD9HhXONQPseZEWSDUrGxGLYQZT4fG1MibeULIZTV2PE9SWu/aMx0/uv4+vb8tii5/AM20M10eVCowV3HMKrCU3mnEqDJN4Cx2s08aCgTsHMY37uXISDrX7IARv3B/w2eeN8fr93+Zkex/rchMcb/PYOBUKrEJkKeXzlDw57/GVRnMpsKxHWH//93/Pk08+STwe50Mf+tCMhlNLzXQpfn2W6nT5/9jYWNN1oJbTqhYoNnR3d5/zpvNXNRrNcsCIZLCer4NVRrOlGvAanuvumD2aBUJh+lde+MuYldn+605JCl/8CqrepapB+T5BXfadEALLXNbDgTmR+Tw/9f0pjKkC9vAoyVKJpC9I+qHQjufiyeDcG6qg3DIcO4ZnJUk1KUR5pm9TWPXnedXmQdNcPjH/19FoplFCRQTWxTpYqQiocRHQP2KR9CDpgaliqHzuHCtrlGHgNwisMT88GOSdAlvPHqAnN4wgbLRXj7BtytkJAj8URq4680PMAKzKoaF/GG4cFtw4LLhl55tIOsva67JoHo1PYpQ9DE+SOJ3F7Mhc7F3SaBaNEIKS6WEH+fCBBx6greDjWhmuOxPGiqSLAVuOP8aas8NIocgUs9X1VbHIeME75+uU3YZlDAOnlQJrhT237aHt5t203bybnSMWO8+aXFbZ3Ta3wFUjR1iTH+c3XvpBjnQGHO6CQ50KVS5xNrdwI0POzTE0PMTQ8FBr34hGswxYtldU//7v/863vvUtDMPgAx/4ABs3bmz5a5yrhH9ycnLGYz09YWvvuRpYZbPhESneJNxdo9FoVjxCNE1aXajAqqSknEyDZRGYYkbn0mTduFIYBl2pc+dvp+LRi+JAQGkOEfeSJPCjJWeGseIjAkr5HPZIFqPs4RgWxbhZ7TCtPA9PLuDLWcla943QFVu04PozkPDDi6Z7tiQYfGeaN//tK8LFTRhLbuJEZieTTl9L35fm0mCGg9VcXM6eMKYdrFHBXwoHOZVttoqmjtDBGj1vTAusjTQ6WAFkuUxx5CyFs2eIeVli0iXnhE54FVnXoj2+ugXWh7fGyRwdIXPkDE5BR6dpVg9/8BP/Hz9z5SBpJ40MfKSIZjbHJvNsOLoX58wEz1gFUHkKFuTtcFySK/tzbl8FAWU/egyPOTbGUkWK7NkT3nbvhnQaYdu89fEwa9WR4SJ2z1oSXs1kds/BuzmzQIH10UP3o6TP5qP6XKRZnSxLgfX+++/ns5/9LADveMc7uPHGG1v+Gh/96Ed55zvfOacT9YknngBg3bp11ce2bNkCwDPPPDOrQ/XgwYNATYzVaDSaVUWjwFo5FAYsUGENAsoN4mfKhcOdULSjF6LpcmFeuVOJuB1RehVQmKWRFoODMDQU3i4hlO9Hm1yt8IgACMV4o9JIwlABQsnqcyqQ+MH8v5tKhhc9N59K1rqvB1S/kJYy2CWv4rInn+UXHje5a8dPcbLjRsaTm5nqeAlPnpxozZvSXDIoFEPrYhzqggPdIExzcSWX9REBdRSNJL/8z2+LNBjRzKRZREC9wDqaCG8n7TIiMXt0zZVnJP/adoreqbMc6A7/toFBeI5SCiybjvjqnwBUopavrtGsFoxEis67/wOaNLUuWor41AFsv8jTPYJRR5Lwaz0ClOeSd+cWWPE9yg0/HMe5QMeLgQHExpl58lbvOuJeTRh1fEV2Hk7cadTQEKZtM5jr5lVT3bR5y1KK0mgWxbL7Vj/wwAP8xV/8BUop3vjGN/Ka17xmSV5HCIHneXzjG99o+vx9993H6dOnAXjBC15Qffyqq64iFosxOTnJvn37ZqyXz+c5fPgwUBNjNRqNZnUhaKbHLTgiQMpqk6tmPL4W8hXXz/rJ2asG6jFjcUxV55KZGKdw8HC0mdWlTqPAukqaXE0jAoVRJ7Ai5YIiApDhunkndLBOO1frZxVOZV5Am2fy5NptHO4KJwTyjsAA9j87v++qRjONEkRL0g2DxCIE1tkcrL6RYsq/CaVWt2tysYQCa+3cFHgu8VOn4MwYrkl4LBAgSyWEOfOznHbPA9z3t4q1E6PRBYQIJyoti+55VGZoNJrlh0gk+V76MLJh+FSwIFWUvPmZ45imhdnewW/8OEnCK3KwG4b64N4n7uL3vvtHzTc8OAiDg6hXv5qSiko18XNktrYSQwksPzqwN3s34CZc8o4g7wgcCRPF+TvTPSGJZTI49hae7ftJXjz5Jt74529q8Z5rAAZOwcBweNNcWJaVwPrYY4/xyU9+EiklL3nJS3jb2962ZK/1hje8AYC77rqLBx98MPLcfffdx6c+9SkAbrzxRnbs2FF9zrZtdu3aBcA//dM/IaWMrHvXXXfheR7pdJrt27cv2f5rNBrNRUOAaJrButAmV37VwWoKk3pfbOOAdeeJJ+e3a46DGYTlSkagwPcpuHJ2l2oux+Cbsgz2D10yri4VyGgG6yqICKhHBIqbj5XpPyHpPyHhPBysCsg7iTA7sSFeYLrUd9zs5Whmw4z1xxfaVVdzyeMJEyUsBGAiwDAW6WCtZLAa4XfXRIQ3w0QFKaS7afE7vYpRhsFVow5JP3TFK6U4FS/yTJukXCwAEPg+5an5/dbXTtQmXQoWocPYNEkmYpeEwDotxnCBe2loNEuJkW4jlT8Fqla/tX0UENBWfI72U8+x87jHzv3jXPfwMdpLk1w5GsYIJD3IjueQc4xNFIriN74VTvpW9IbGGKwloRIVYH/hy3xtuwoz5wErX8LsWoMtazneApjI5uY9/vfa4kw5DofW3IhrJvCNJO0T2/EDnV+/FHzq3xQHOxU/PnAvnbd3XuzduWRYVlPYn//85wkqP7AHH3yQhx56aNZlP/e5z1X//eSTT/L7v//7APzmb/4m11xzzTlf6/rrr+fNb34zX/nKV/jEJz7BZz/7Wbq6uhgdHWV8fByAm266ife9730z1n3DG97Avffey6FDh/jYxz7GO9/5Ttra2vjWt77Fl7/8ZQBe85rXYFnL6uPVaDSa1jBbRMCCm1wFlOvK98PmVDNLpm49cg/pcnHG4013zY5hBQVc2sNdE4KJZDvkz85c+P77GXybz1AvILIMLHD3Vyz+zAxWc4V3Fb+qctFiKZBKYQW175GSEs8/RylePb5PyXKQwgxt2ZUvdtES5Oq0kJH08xhLKPqHQ0dKPhl+pm4+T9mX84q00GgAig1RKUIYJBcjsFZ+38l/+hxsfDE5JwumiR/4jOXOcMif4tbPDHLPO3Q392YEpsA1HZwArjsD8aLgpqkYfbk0e6wJ1HhYIluaIz91e8W0apkW6ybOYigPKaJ/5xs2ZBBLlae4TBjoG+CZTUMA7OwbuKj7otG0EqtvAxIfSx4BtmEqONwVPve+T30lsqwRi3N1qov76h5LBCZTZZ9MonnZvxJQNGvBzUIpkrELFylidq/hocQEv1JIYJY9kuN5RDxOwBSqslPZUpby5/6R4u99n6T0QnF2DmRbiuG2PnzDoqMMoBDlIs+NTLFpbcfSv6lLCV9i2DZXlA1OxtyFhrhpFsGyUgCPHj1a/be/gIshpRRepSnFQhxUb33rW9mxYwdf+9rXOHDgAFNTU2QyGXbt2sXg4CA33HBD0/Uuu+wy3v3ud/OpT32KRx55hEceeSTy/NatW3nTm9407/3QaDSalUVjkytV99/5o6TE7eqBROhqkUGWq88cJF1ezze3pQDomzpFV2EMEczvIlTYNq9+epyDPWtIevDg5fCPl7XjFbM83GT5x3rBtQSucekMPQLfjzhYESvcwZpOw9ksoELx/9rrMAPJ093h37R0/AFe5b5s3ptTUpJ3UtX7wrIQgUfKDTCCc1/cBKUSUyWfWFoLrJr5UTLtsGwcwDQxLRPbPP/fpLDC72nKL5GUZfqHQRqSfWugx7VBdfDUqR+0YtdXJb5pIg2LogFYFolyQLwug/U9PxH+P/Oi3cwmJwTTx9RkGtu26Sg+x1hyc/X5TGmS6y7vXpo3sNzIVRxvl1jeuWZ1Y3R08rrHy5y5+WkC1gHhuOGY933am2QrJ3/rt+n45BcwVSiaJgKDXMlrLrAODUHMohjrgY6a8zvpXNhxxVGnyIGgCDbc1JYB4Pdf/1HetedbKGGGVRECJpwkyeLcTauU76MMwel0H2bdkFspxcnTY1pgbTE73BTJrjZuOwGD+wXByDB8Y/CcIrhm8VxQgfULX/jCnM9//vOfP6/tXnvttefc9mwMDAwwMDCw4PVe9rKX0dvby2c/+1meeeYZACzL4tZbb+Xtb387jrP6S340Gs2lS73fcXqctNCIAAKJNzEJbmVrGegsZhk4+QRDa3tYlxNk7TN8Yxu8908emNcmhROjrTzJoU7F9hEwEGA2L4vx4za/ONLN+oLB006eI5NDcNvC3sJKxJeSdH1klmFgLULMudh88CMDfPwPh4CKQ+obX8d62zsiy7jnaiZRj/TJO9HGNesKHtnyvSBeDcALnwOzzrK9bQyeNsLXeOjofbyutIue9AUo5dOseJRSFMez9J+tXDjv2kXCsRblbBSVCioBXDtxjDPJtaTKte9rj2djZjdUY1H23KYvuOop2THSQeUS6fm74O7vEvfC6JmXv0NU3Vu3zrJ+2knzzKbw3zv7BjCeeoK1k48znohjqB5SxTO85vBerDkaZGk0muWNEILyxATxAtx66G5e8kQ3qbOjrB8/CX8xc3kj00naLQChRpCQBpPl2ccmyhCU7Fht8q2tbVHZ3K3C6shwy7EcP17XDviMTp3mh8NPc8XZQ+ycY70gP0WA4Gx67YznTo3MLc5qFoYKAnaU0yjgeOb5fLP/coypCbYWJrhEpvUuKsvKwbrSuPbaa7n99ts5c+YMU1NTrF+/noQeLGk0mlWOEIJm1/4LjgiQEq+hQYgtfUwV0FE6Q1t4PcsJq4S1YeP89s1xaCvXcvEcZdBJhrPGzEmvUncbiSAUd69yU0x68++EupLxPZ/6P6BYaU2uzjH7LkwLU0Xz0RdUFeP75K1ErWtbewfposJAsnX0CI/0hQ0s9/bB9ZVoxYQPZiVCLFuaYqp0aXyXNC0gCChiVjP2ABItKAO9/f7beWvWJZU9xgsTvTx0xYsiz68JdjDuHcJb/U3sF4RSipITnRwxetbgXBfGj5nG/chANlu1OXv2YAy+FFscpjv3EK97Kpz4i/sOYoVHs2g0lzpKSu5JjvLKs9A/fAKoTXA1YrZnSLt5IANALBBMleYQWIWgaMfDzGZAvPBFF9zB+vgGi4Jd2cdKUpeR6aK9OAWVKK60b/BHu1KYeZ/Hb+9k/MPjTbcVTGWZcpIEwibRMEQ6M54nV/ZJX8AmXquZIDuOrQzOpNeQTVxObwGC9gzfS/fwpou9c5cA+lvcAtauXcvatTNnYzQajWZVUhHnDFERVSs61IKbXAUBnhE9DZmBX3XE7nemOGDnkULN+0JU2BWBtW5XemWCLeUrUEpVXWFKSmTcBmrB+rdMti1o/1cqni+rA3agksG6ggTWc2EYWA1vx/MXIIhISa7ewfqSl5But+A//p3NY0cw1Zamq1XzWaWc05Wi0USQfuhSqiPZoovMtVPhgTDmneLFRx9kb++Lay9rxIkVNuF1HG3Ja60apKRsRSfkHOlXqzZ2b9xdfXw25+8HPzJQW4ZKJUUDajUdczWaS5TAENz1OcWhLjjQIzDbO9i5v7nAaKTbSZfz1ftOIMgWyk2XHfzpHJvdBANurDb5ZpgX3MGadtIIqxLxUan4NTNdpN1aoytlmVwZXEbZf4LH59hWMDXJVKwWdyAFlaa5gqBcYniyxLY1uhFeKwhyodHkZHtdI1YFJ1xJyZO1JpqDdc19ZzMvzGcZTQQtsGo0Go3mvBAowiLU88tgRUr8OgeraZjkYz7jMgz3H3EkE44i48//VCWcGJaSODIPpMg5ocuw/2Q33stfivPdsMVAkJuKWG4DTHDdiAi7WpGyQWw0DKwV7KTac9ueSLSDAIxAUrRq9/3//Tsw8kxlhbkHiEr65GOp6kSCMAzSFddIe3mKTWOPI7iOnFMTVesjFwwFk0XtYNXMD+V5lKyowJqItSZmStRNevVOncbxpyg64USSQqHcy1DquZa81mpBuaUZAmv8Pe+GF3w0vHPnYJO1ojQKrwKBL6JnSMNbwKSPRqNZlvyPn+3g41/KIYCbtu2ec3wh0m0RgVUA2cl884WlxAxsphyzMtaGdtO84A7Pgb4B0s5Q5DGjo5NkOUvRCsfXAjDty3h+eQ2H8qVZtxVMTTIZC3NqBVCwQ4HaNExUucx4wZ11Xc3CUG74dyja0cpqaZucPjvBFeu76by9k387liPtpHUDwhajBVaNRqPRLAwhuHPoTvYlX0ogTPorJ+ZggQ7WwPfxbQcq7h4ZSCwZOv96CvDWJ1IEfmxBJ37hOFi+Iu6NM91wIOXCM903cujx73HNYBjwrop5QCGFwzPdL6Tg9JApncXL5XDaVreT1W24sDcNc3U5WAEriDpIXXP+rg/lN2SwCkHH+r7q3Z78Ad78+DF+3HcL0EZ7aZIbhk+xb+1VAOSTFoVSc1eKZvUzWCfAzSfbVJWKlOKpmqtcCBKJxef3fq+rwAfq0rINFFtHn2J/7/MBkAZMTJ7lyXKTC/xL2LGiymXKZp3AKgTxusiG882rPeIUSYs4dw6E92//7Uvoc01XXGnbBi7qbmg0rWbP3gHYNn3nHPFFToy1Xrk6MRsEkvFsnkApjPqJ/aEh5GZFYKZB1cwLhiEueERA4wQ2gEgkWZ8dBgJSbu0c88S6n+QVZ7+J8v2mMQlBIcdUpYHo9lF4ugdK8fD9KLfEeEFPTLcKVQ4F1qlYeD1zoBK8KvCxfvA0X8j+Njk3hwwkv3Rrlo7YENw5WD2/dd4e9q4Y6BuYtZGjZna0wKrRaDSahSEEw7lhctYkgbCrWZUL7XHlywBVVzqZdgkFVgUlEfDfPngt0hALu6C1HVCKpDsKXFZ9eG8v/LBvF8diB/keEJTCMClp3UDB6QFgIr6Gvc+c4fk3rG6B1ZdB5L5tXfymCa1mbazWjVYGEikW4NCVkkL9rL9h0L5+Pc9VQsMCFF/dUeKyibvJmjHyiTKvfOf/Jf/0Y9VVikXtxNDMj6BcoihqGazCtEg6ix+eP9lWxswGBOkEsuxTnhjnp3J5Pv7651NKWBD4xAJBjI2hc/9lL1v0a64GlFuOOFiFYRJvwd/jiViObhvW+jZPJovYG5tHjWg0mhXEAiaghBB05qLNnGS5zGTRI5OMuuZjgcFbj3Tx/26qPVZ89lv8mnjFona3FQghsMoFevKjCNYAtWqegr2bwonjpK7YPGM95ZaZiqU43B2O+/MOTI8+lZR89Dsf4w9++GNAN15cLKpcQiEoOEkgFLSnCbITvO/PTzN5k0/KBRoibAbvHCRXiYAYGh6CoQuyy6sKLbBqNBqN5vxQqnpeVmrhGayNuZinLZcgO46Hz+u/l+e3Pn/PgndJCMGG7k2kvXGSDTGYjhK84tBljL369aT8KVzDYjwZbZ519EyW5y/4VVcWnt8gsC6DrrStxnnjTyAefxAIB/DuqWHYO1TNEJsL6XuU4kmmv9zCMEjFbN79r8NsPa74b3sDXvM2hZFqIxUUeTKR5z9bUQG3WCpdEnETmuZ8/A+Hwn98ZvDckRTlEqVYnYPVslqSsxcIsEcnYXSSawKLH60JwHfZdvbbmM5rGOqBbtfESSbJFr1K25UKQ0OLfv2VSuhgrev8ZVnEFzsJtWcPzp2D3HX0XiCsGrhUaOaA02guVZLrejGDMtKIhTEtnst4g8AamAYvL65h//obIusqYxlVxtgx2kqnoCKwTuObafYcHOENTQVWlykzRv9weP+BzdHjoOUn8BVNm+hqFoZyXXzDQSHYPhqOZg91hc+JvV8n6NkCPDHr+nf/XXh9Zho5yOo4m4WiBVaNRqPRLIzpbMpI6qqqjzSdF54XVUB9oVBTE7gqwGzSFGTeu4cg7k8ggjCHdRoDgRAGJ5KdXDk5yTNdG+kfhgfqNNZ/ePQr/MzLd7ZWGFtG5bYqCPCC1e9gNesyZRMlie8HkMvNSzgquTLirEYY1bI8ozIpIAV87IthE4EbSxmG/y6acxW4LmU/qDUS0GhmQZVLkQxWYVot+97Iys9gygLTtEAG5JxJrpjKQU8cgKsLccaLHm/een91vT1Dl26jEeVGIwKEYRKzW5NRPS2spp1L9/PVaC5lhBI4cpKisQaBQHkuYwWXzd21sWq5PU42qfBUtJrKtWfPN73QqECi1DPYcgePrLO57kz1GZ4dLzBRcGe4cqVbJldxVALsunwX7N0b3hnbS/ayMY6UDxMwdWHexCpGSYk0Yggg6VPtSTDNRMdaDnXZFC1vhjnGCBSJWAJlGGwpx5nYbOIUXUxXYk1lMds60MyNFlg1Go1Gs0CmxafoSXmhTa5cPyqwKgJ2jAQIoC04f4FBAG94WnE08zQPbrwRgG1jkPBh34YEDz32LOLkPp7a8UaChutmS0LBk6RaUBJaZTm5waTEbxCv7SZZWSsdx4y+R9e2Z1myxnR25s5jMTZxdfVxYRgzBC8BERE+2ZaKPK88l2J9p1aNZhb8UpFSfUl6ixysAEOV6GDLqIl6geeRKU2RduOV13MYH5/kx2trx+PBn651iL7UCjUbIwKwTOLW4gXWPbftieTzajSaSw8DeNvpBE+1Z6C/H+X7TDRkj7rpGNLwUQ3GwZIdnRy/mHz4vVdz9Q8PsuvIXWQKN/Bs51aCyrhLuS4nsqUZAmuu5BPUxTUJ0yTIjhNUJsSdQGDSrgXWViAlvhGjfuSbq/45FIZp0S3XIoxTQPR7dcOw5N6doYgaP+zwyGWDXHvmMFdNPkdqfFQLrPNg5bYN1mg0Gs3FQUz/r05SbUFEQKC8RfhWa4iP/E98S7Bp/ChXjO3HDMKA90JFR5zovoxCLMVEvJ2iHX1FU8FUyW+y1YUxeOdg9UY2W7tdZJT08Rv+TKvRweo0CCKuNbfAOnjnIEPDQwwND6FUQwdxx8QQgkRp9jIpK5XGCGoXScoNBVbNJcbgIDw6xHtekuU9gzkG+4fOuUqh0OBKsiySSyDM7+wbYGffAE/HC6TcaGOryZGxyP2cKau3Sw3luVFHsWESW4XHSI1Gc+ERf/23PHBFjr19sPf03qqDtZGUl54xHv7ibZ+4IPs4H/7j3ffwVJciHsDzTj5Gd2EMU5iYwkS5ZU5mi5HllZRkv/Cl6TwxUIp4Ik5PfpR9axT71igMX2LSfpHe0dISuSa4ACjpIw2n7tonHPhLEd6UY/Ga3BYyPb280rucvYkC9x+7H6UU/2fzOwC49Wia721+DTknxQ8u7+fuLS+mkO66IPu/0ll9thWNRqPRLC3Tzj21yIiAhmZLSrWmg6jZ08vXtwT8xNOwNrefn953lEfXvbb6vJ9Is3frrur9hEdVNHYDRbbo0dceX9xOPDq0uPWXCt/HVw0OVnv1DQUcMyqweqYzy5IzUcqpNhwCSM1D7DKSaUxVJpj2C3ieFlgvQQb7h8gVJsJ46sAHN9d8uQ+EHXo//qUc7f2vhrbN1QxW27aJt6gk/eXvCH/rHfE04x8Ovajfu72T9z8V3a+pqeb7eSmiPJd8fZM70yQdW33HSI1Gc+Exkikmc0fJiUoTVs8jW/SQgcI0amMz10wTV1CsDCnsoEg6nWyyxYvHQ51FyhMudjrNTaee5Y5NawFQz36f9h3XRnLolecyNZqFDbX1M+0pEqVJcpVGs3EpMFndTWYvGNLHN8Jx74Fu2Hbah+nxaeVrduOpHn5weS9Hu9fwMgmP+D9E5XOoj/4+hYEevn3lLaRcES6uFMOpbn54pswrtcZ6TvSIQaPRaDQLQkw3/5kREbAwhdVvyGANWLxzFMDZchVKQHcR7ABSXhHHn4K6gdvZ9FrSrqKxe6YVwFR5kfsxOIi5eZI//raJAgJDgBAY8uKXdykpafQJr0qBtSH2oGzPX2BFOZHJg0RFXDHbO2A4dCHv+deOWsOsPXswDj6Jqcp4hGXYyvcpeRf/7625sOSKWdqlxa5iJ+W4xePJPIN3DkY6IqsgYPOYJKnCpOn/b2OWvpwCIUlLk/aE3bIM6OnMz4G+gepjA30DTImJaj5ruN/h5JYCbGGyzkpjINibvPSEV69cptDZU70vtMCq0WhahEimcGStBF5JH+n7ZIseXSkHJX3o68O1EsTrPAfj8anl1zRTwKRfgGyRy2hj61mJQhGMZilOToXNEysxAcotM2VHo5QyPV3ESrXPIhkYmOjy81YQZrDWxr3f2yDJCAdEOLZNuTDcvh6jMtQ1gIHyCxg9+DQFHJ7rfDHXnhW1qwUhSLzhDbx4S/cFfR8rFT1i0Gg0Gs3CqA7yGiMC5r8JpRRug+Bom60ZPBpt7eyL5YA0AYrY2Unaikc50H09AE93w/bRsAFMuqEyywxgsrg4J+3Qmcd4xdoe1qdCcePk1UkC02LtqWEW6Ys9f6qNtgL8X/jlyFNWC/IFlxuNEQG+aSKFwbm8qNlSltNTY1xhdGFWvp7JeG2QWoyHW2gbGIg2LLMdzKDW4VcFkoLbmgkDzcpioNzOW5+yuGs73FpI8f1C+EWaLg209+7llW4oxMe7Y9gNjp32VIxWMNA3wNDw0IzH99y2h298qpYxnHbS5MpeeDRX8IbxTtaXQ6fL1lKcYGoSo211lm0243e/+wd0udcCldxa0yQd0xEBGo1m8YTVLh43HcuxvyfO3gP3ceS57/LyHR8LBVY3HJR6Vho8uP5M2HTolg//yUXe8+YEFddtKe5xtKOIb8QBxQ/2fpHX3PThqMAaq2vuZ5lkerqwy7VJPEsJEkEbk6tvSBqtarvtAryelLzqeIp/3BHenYjDFZbJWNHHDGrOaAgjAyA0zxwceoITl99AICwKVtggK0Tx0s2dre1PsYrRn5JGo9Fozouog1URLERhlXJGFmhweW9NvE0vrsvyk/Ecx6byfLkd3l0IoHQAI72DbWMWgrDhVSigKUxVG2CYCrJN8rCqVIVKogJbHZfnBXFlMpbI8PWrXxa+jmVxXe8hBj0XsRA3ZYtRAry6iAChFLa5+kaz9QLrlaMKFHiIOQXWnJtDoRAkIo+nEucWvIQTiwisSElRO1gvOQwFfX6Ms6kehtv6GE/GKR87wa2fGUQI+Mp/f5i/edlaHl83gG8kGUk9i0sMJajOUGVSSz8Nc2P7Rg4UMuGdK/txPYmhbL711Rh+Vydfu/pyYn6Obu8E3olniV19/ZLv07IhiP7eHcvSGawajaYlCMuiaCquKGaBOEopHCWqOazTAmvZqo2BvXyezs7lOcn1yneFx8b3m5cTey6H74Tnr5hUTBZrk8zKdZlM1N6TeOkgHZ3tGMWp6vgb4Gf3GQz+qAzfGJx1jL1Sub8jrIDqvL2T8Q+PL+lrKSkp96ytup67pMNbb3w1f/XQl5CiNsbY20ukkO8vnnqAF7avrd6f7l3x8iN72LLj95Z0n1cTWmDVaDQazcJo5mBlgQ7WJlmgSkgObQ7Lg3bWlbSeD6YwMaRPuaKzTRkFunLfJuG/hpIF+9aGzgCAgWHYe3UGgP7efibzxeYbnQdKSqxYjIn4er5+9QswpjU2KXlyzRZeMD5Bau3aObex1PiNEQHGMis7awFOnSAiFHxjG3z+7TYlM8vDc6xnKmBaYK18z5OJcwtewnF41YE8x9srFxRjD1Pa9cLz23nNymRwkGs3JLl2ZAv3bx5gJB26RCx1GdIr8sToV3E70xztvpGC0wfA2bYM0oC+up5T7anELC/QOlIf/xPEfU9Vj9nKdXlD9nJEJuC+LYOc6gjtLZa4nGDq4jfnu5CoIEa80uhQliZoi8/dIG8h1EdFaDSaS5NJS3KgO4sUvQCsK5mM5cIJWuWGTQ93ljYwEhTCmCkhaG/hcahVjH94vFqZ8aMD+7nJ7yQ/nafqK0bylUnnwUGkCMitua52/WBZdCRsPvlT66Guz6MdCJRKwtDQBXwnS8/mMcmNufC8/8W+iaV/QelTnsrTL8IKmZt+/p0krBztxQNsnLohTApoMvT3zSTKhECE58AD3bBj+Gv80+s38sKOzqXf71XC6rOtaDQajWZpqeqr5y+w4nu4DWf3QCxdU6D33u/RlyvwqgP3znhu8/hz9Pf209/bD0Cx5FL2Z9mXoaHabXCwdqsQTIxRsmKcyNzIvrWQd8IoAoUiEAb5xea7LhIFfPfo/bUO4YpV6WC1LDPyhRQAYu4LlP4TkheOp1AiRcEmbDpkmrSlzy14CSdGzC8jKq+lcjny5Tmc0JpVh0JxvdvOgTXbI4+/5JjBz38lYPdTJXwrQS7WR8KrNNdrQkd76xqZDPQNMNA3MEPYs5MJEqLmsA6KebqCDCc6Lsc3w99J0YKsvY7f+MYftmx/VgK7fzRF0Qrff9EWpBLLT9jQaDQrlxFHEvMnMVU4qWsEipHhcMZfuS5lw6JUbcwZjmM6lvlxaNyW2HIcaYRj3muP5Bj+gz+ujo+nbIfAMKrjKsOJ0RG3+c3XfxRL1mYYhWHwkdet51U/XaDzD1eHoOedOMbVXprePKzNw3sPZ3jN39y6pK+ppMTtWhNWA6bTYBgkO9poKx2hszgWWXY6DssyLF5w1Cdn+GwfhcNdkFXH+H56lD/9yH1Lur+rDe1g1Wg0Gs0CadLkSi0sIkDJmQ7W33vF77LzV/+qJXt46Io0gZHljn+HtsAk8H2OpwPW5Ee55vQTPN0dZuy1laa46eTjfHV4qDqz/vzNVzJV8omlG8pCBwchV8mLmiXCwB89w/61W/FMG8W0uAqBADOdpuguLt918SgCYUbur0aB1bAsOpwEUljARCh6GnNEMyjFYK4LT6Y53hUjqPtqZtrDxgw7989e0iWcGHG/FHmsUNQC66VEgKJstRNYMwVSNxaj3VjPWDIs85zOP2vMgAboyCx9KahwYqRFQKHOZyGNNGdTPZHlFIpTU0s38bVg5hHRslhKdnRCJT2PiBCNRqOZL8cSHs/zwsqAXGVY8sUff5Gfe+n14LlMOHXNoBQYStG2TBvtTU/eveLvb8U5Mkyucm57OuPzbSvHn8Z+yMN/63J2x7XQeVV1vVgyTswycNsyxOQk0kyHjXJti7efuoFXHBqhPKQIPlDEiC99VcdS4h3aD8DZ9FWMpK7CDMqsnZhY2heVPqV8EXwT0mmEYZBc20PCcvipx7/HE70bECrAKZ1gpG07R7qv5bpTsiq2AmwdVXzw9v/Hdet147GFsvquqjQajUaztMwSEbAgmkQE2C3OuSvGzbAp0e7d7Ny6iy/0jGCNTnLV8JNsO/ttdj/zA1719L0kvRKl8gg5NxfmcEqfbGl2IXTwFySDP53j4QP38KMD9/KjaVfs4CDer/0KB7s2ceOpaWcCVTcU/f0USxdXdFNAQLR83lqFEQEYJi9cv5P+3n7SnsBQYk4H6/qpgLV+jNH0TvpPhx1WkRIzkHQkz52ZK2ybuNcgsJZc1IJs3ZqVjDQUxzJrwkmVhp/U3iscRntfyam2LraPQtKD/tO15wMBgQFlL9vSkvTZMGJxMmadcLp3L2UrQ9nqmbFsYK67ZL7HSimKDQJrKn7xMrM1Gs3q45HfOY3jT3HD6YC0W5lom5xkrOCi3DIjeb82mW+apIMS5jIfp337nfeQU6MYqjZ23ujF2aq2kcuk+esXbSHnEFZOAd3tKYQQGG3tJN0TobgKIOBHGzJ8fNer+NNbfppv3v19Jubqi7ACOPbBd3HLsxnOtF1L3rGYjKc4M7luaRuhfvrTuBggZfhdEoJEJsOUFZD0JDc/d4ybTzzH4/FJ8oljGIE34zyvpGRjV+sqai4ltMCq0Wg0moUhBG99xOUFz3pcd6p2kb5QB+u3j95XLVVvdbOl8Q+Pc8thj1sOe6HTac8eyp1t2Nk8xbNnedOPhtk8/Axtw2cwlYFdV6KkfJ9ssbnA6sUsbiy1s6XU4GoaHIShIY7kfYp2+Fyz4XD5YnSWr+wbQ0MoQYODdXVGBAjTwBbRiACL2Mw5gUrEQ98PnuCGs9dQtjdE/m7pcgFDnPvCRpgWqVKBfWvDfN9H1kqk71P2daOrSwVPBLhm+6zTTm3SYt+6sKXvtKu9kbWnD1WbUiwlIhan04gei24aXodv2qGDpXqtK0C0M77CL3Dni+rppiRqTjGFIn0BMnE1Gs2lgxCC76bP0l7OVR+LBYLsVAHllpmqOFgPdwse297Bl1+7MhyEMu4Q82vl5wUbgo5beP/b3kZXeT1yeqi5axdb1oXl/0ZbO4nycxhBdMy9KRtWiX3x3n/lJ//uf1HyllElxQJQQQAIhtv6Io/HfINDZ3PNV1rsaypFALhm3WStMIjbJj/M1HpMmCWPzz77PD72pk+yvrQPMwgneg91wcEu+O6L0ssy+3clsDz95hqNRqNZxlQiAlRjRMD8t6A8L+KkRF2YZksCQCk2PZMl4YFlWhR2bkR4Y1x7NhMu9B//ztjVW2asKwkYv7yTyyVcLsFOKvxCZbAyNMTbf9Inld7E1aPVtzSDYvkCRgRMl9MODTH40+FAbpt5ALLbQVUaNymwzeXtjDgvDIu4CKDyHesIbHaU1yHdLEGphBGvNa5SKNK0s3/tlUhR93dTivbS/AfAMbdQvYAoxU2UlORdSdzWHchXHQ2l6kop5KkTeNddBlDtiiwFkZK7aa4ahae74QXPjfPEGp+4l6btxFNcdXiuFmwLY66GSiIWo/NTfw7bdocPpNN4pk2zo5YATo1M0ZVa/aXy0rEo2HWOHQXpttTsK2g0Gs15kLUClMojRXt1Nn58LMsVnkveqU3qSAOEKM2yleXFH/2Pb/H+T76HohM27woE3HAajmbqFlIK9u7lire+FgAj1YZAEffOUIhtCKuHCJsrhVE6PqkzefadzPL8K7ou5NtpCcots85zuC8dNredHhvYgeDwcyPcsCHT+hcNAqRhEIg684QhsE2DA2mX5LEcyjKIFT3EsXswH38Eu3SMLWMZhtu3cP0ZEK7Pf/ngb7Z+3y4RVp9tRaPRaDQXBLGYiADpIyMl2wrHWvpT0lM94YA15YLVkQmzif7wdhw5WbcnNC1Jco2oUhJr7wgHi0rhxyyucbsoOl0ERjgLrADLK0fWKXkXqcmVlGHJO4LAiM6trk4Hq0lMRL+fvhGjWzrVPKxppFAU41vI2wamCoP9i3aYjbYxe2r+rxlILFn395b+0paAaS4Yg3cOVm/NUG4ZZQriMlltdHblSIDABxF+l2Tl/4rwVrTh6jOP8V9+8D1e99i/cdOBB7GCC/R9seyIe+pcjE0VlnBnFsbg1vvD2wda2wDFPfwUf/iKTspmLRJAmCbphI4I0Gg0rSX5wt3cs61ELlY5PwSSiWwO5ZbJ1cWUSAGI8uwbWkZYPb10+cP81BNnsRomFg0FhzrDicUH2o6TyYQOVWGaGELw5v0jpN3w3GlWOtwn/PDWVlL8zr98gsG/ff0Ff0+LRRULeIbJWLKL7SNUG5sBnB4ewZNLUOUkfbxfeHu1oRjpNEIYOJWxfln5uJ6LMMLJf6t3Pb84pDiVfIwnu4/w8LoSe3ZB/4aV4ZxejmgHq0aj0WgWhgDLV1gywKizrS4oIsD38YUNhEKmUCy502+gb4BXvX8IgPFvDFQfN9LtkS6mCsi7M8uR/FPHZzz2b7euwVYGbQct8k4PBbsmO5dx2d95BBXfgWVY9AOlCyy4DfYP8fGns0ihMAMwCiVE2gHCQd2qzWA1TWIiOnD1K02u5OiZyOP/ddtByvHX0NGgqXcXRtg6PvNvPhtCCcpmCdeKEQQS5fsUmnyPNCuXj//hEHxmEO6/P9LoTpVLBAh8s+aMbst59GYfIO69AAhdkHv7YOB0+HszlEtXcYyuvGK4OImhmscGLAVCCBJuCVNJpJh53L1qFJ7uCZ3YAGO5i+SgatLUSvnhMTSYnGjZyyjPpfj9exlLbqanVkGJ7ZWWffdujUaz8thz2x5+/Xd+lmN1h9ZsoUwQd8k7NRd9YIAwVoaD1ejsRoqAk6mHeeHxl3L31pnuf4Fi0no88tjvvv0z7P3wr1C0rifnGGyeCMVY6ipBEiPjJM9u5OiPHmHTTTcu/ZtpEapU5Gyyk6QKZ1cDozYmlKUip6fKXJZpbQyNkhIXEW3GaxjYlmCgbwBh3R8+5oTPm91rCTyPt+2zYN8QSZ4g/fhhxCo0X1wo9Cen0Wg0mgUxnREYiQhgHhEBg4Ng2+HtJS/BFzaHN3dweHMHvPa1JC5AKfVA3wADfQORx4xUG299rMDRTFjK9MPkOGVfzphZFpU3eDoV3gDaAou4MgiCgHwsLAHKO+Etq04R37AWqz1D/9ZdABQvgqNx26jCkNMz5yZKGGGjMiEgk8FajYMo0yTe4GCVRgwpLIJCzbmnhoa4YiqBZ9YuaHI2DJz4Ibc88z3iCxCWlGNjBnXLex6FFZobppnJ0PAQOTfHo8NDPLpG8mg6x6Pp8LukyiUKdhwqYqVSinHDoySmuHziJBD+/gSCo1d08Nh6gx86j1LKjnPQPYt0XUQmg9l+4RwjRqBINTZms0KH0dM9kHdqau/479+O6uyMCp4XgcH+IQIDhtbBY73M6iheKP7Z03xm6LOc7NgCApJ+eNtVLs0rg1mj0WgWyntf9EuR+9mCS7lcxqurMpIIxApxsAohuK+7gB2UeOXB+0iVwwqgTRPh80n3LPnc1yhvb4us51x9PSm3QHf+8WqFR84coaN4nKIVnjtffEzxouMmX/3433L82ZMX7D2dq3rlXATFAqPJ5tUWynU5lS02fW5RSInX0GnTNE0sw2DPbXtou3k3bTfvhoEBAIRhEIyOY2fz2Nk8qTNTCFN7MBeD/vQ0Go1GszCmBdb6xxRhufw88QyDYLLmGsUwiC9xREAkk/AztcGSkW4j4dUGOTYizM8s+2TqOsgrAaMJyMXWU7K7ONxVRIqjFJwe9q7rYzS1tbqsXy7zf/eMYv7Jr3PPodHq4xc6qH8oncOL2by62ElKmbimw34pI3+r1ZjBKoyZDtaNUxtAbOA//uorvPGVbyJmmexbIynYtVyv/mHY1+tyWfY5vP+fvfuOk+OuD///mrJ9r1f13svdqRvLts4NGxuwCZAQkphAEgLJF0Po8IsxHUIghkAM2CYYXMBgW5QAxjZyL9jqtvqpl9P1srd1Zj6/P+Z2b/earkk6nd5PPe6hu90pn5mdnfnMez6f90dTeCNDv7GZXTAL03GPI4VCpVKSImCC+NbXtvFPtRE+crWFZkS4c6PKziCNSsRz8uahFIZt8UhpPR96tQiYl3mrqqKKzY1bKDi5GbPTwQQ+/CYN4/JqAAbOnDq29CnT2FoZI+INARGq2nvO6Sl68hcH4jY/WQTfm+YF+2nGLkvs8EUTEZYlQxR0GLwejLKtftuYLPeff/ZurtmVh1qi43TnYfbGUizuahyT5QshRG8FwZ681gpFKpWioasnT39Vo4H3qjfzj2/6/Pko3ojsDSXZ54eYCRWdL9Lq0bF1Hwmti8MrFvLY+472mccsrSDV0MQRrYt3bd1DPJXkzZ1d/Mc7Z2M45di6u59eKwfQ2Pjjr/Czj/wnlfn+Pssab1Q8SpM37KbpSr/W3c9NJeKc6hj71smqnwCrN7sBy6a+tQxl25jNbqo0/Q0bxrxMF5sJ2GxFCCHE2aWhaxoOTk6X1uFkEop7/diozI/WPcLl+aCH8zCVheG4fcRtFHbDKSJZ3buVUihDZ0/5YjoC60h65rNjUhW7K9/KkeJLOVUwB6O7i68CLMdi2qRiAt7c7qWx1LkdVd5yLKyyAkLK3bfpbvJ1JRp1JRraNdfgm6AtWB/b+yu2n95OxAtzW0B3YGc5/GF2EVff8wF3Mo+XBS1Fmdyr+8sNdgSOE4900tnSmJMC40x0NAynJyCrUklJETBBzD0aIZCwMwlUvfn5PH5pGf+3voR3ffcKN8CKJ/PgQgFB3SHihVjnQSZ1HKWqHqpOQ/6rL/KtNet4bYrOmvdrXPIBE+PyK875NmlAzdEOlpy0WHLSypR9QZOi+lQSv4qz9JTNvGbFlJSPaUxHH0Xa7bFQ3RViaTKfpdEQG/9Uwq/v6hqTVrWtp+o4UjyTQMo9T2hAWUM9hamz0LpICCGAcMiPrmy07n9YFiejuXXEgM+DcQGlcaqeVM1LoXYiDfX85eONNJhJEvMmc3zlQh5731MDzqcsi2fzO7jypkbe+M52rvzXMD/5yGNMbX2C0q5GtldAu8/9KY7DU3vqsZxzW58eCTvSSWOw5yG+ouciuu34KzR0xMZ+O2yL5F0/gvZ29wfwmoPfXxn5BWimiWZK28uxIHtRCCHE8GgaSU2xo9Khww/xY89z5YKlQ8rBurXMDThF8xXRrNijZujnZJCr/mgeL2bcwmPHsNN5Ojs76EpktT5MJWkN5HG8dAnzm20cQyM94nbM496UV512uwBrBQXMsE1Kk4eJe3Mvs/GUjVIqk2aht6Kv93Qlav1k66i3Ld8ySASCnCxYiKX78FodOe+bhj4hUwRohkFr10ki+gwUCg03/2XE2z04W2oSsaSNbhp0+vKZ1ebOV3ztm/nVoU+RiA59AKA0/Uf/i/Off4/dvTuVlSImKQImju7z2xTLhycY4s37AHT+4tARVG2cLo+fqnp3Uv3Gm5hds5zwb3+PreJMbn+VS44fwdYNlrV3EviHt7B++vrztingnqv83QFEI+v+TqEo6zhFIGUC09lXDDo6BZ6phJyD1P54A5ve89R5KfO8ZIj0edcyvfgCfXP8DZdSiqntJlZWt9xOO05JpAG2vzjq5QshRH90fwC/ShDtTi3zzKGnKKioypkm7wIbZG/TLZvgFuBO9+/7R7Esc8oMTi+fxsfvfZb/Xn8NEfJAA11pNO16ndcnF1I1tXAMSj320ikF1hzWmeJZ3O807fF2rFichs4EkwvGLg+rsi1SWV38NcgMcDWQmt2jv98QPSTAKoQQYmRUr1+H2Lrpn25ULLZzK40+0zi3ue56dZFJRaOsPhnlVJ6bA1ElErRFE4CbK8rp7OBw4ZTuUB0cKFRUn3bnXdbQ3bW2e+RTXWlc0lgHQMCXe5lVjkPCcgZtrdsed584F329aNRB1nLby5+n1dAYnkq6iNWNPesOnqdWw2edYWAot0VyIAXx7o8hmIJ5zbDk8HGafv92pthBIr6efGBGSRneYxZBa/jHohYMYaqekbK2HHuZiu4cV+LCphyb779Sym8WG7x1v3ujkj7dWcrCbm0i4gv1pE8xTMI+k+rKapTzAq0BKIo3ogCP48fIK8hJWTJWuUSHQ1MQTEVzXqsrhoSu0Tw/ha/JTeES634QZmtTML03ETn8+rkt6LZtADiJnq6UyxuX8czsOShNx7/9zywfxeKdliZKY+6Ne8zjDiqTVDY1h7aPrtxCCDEIzRdAs9qge1CraKydlle29UwQDhPyX1gB1pEy8gt45qfug+2aYz1pEo4VGsRPnyLU/hTNgRtxH5Erth5+kYpd5VRNXX2eSjw0Te1RCro/wrBtUOLz09l1nKRZCIBKxDjVER/TACu2nRNgBfCcpwYsFysJsAohhBgeDbxKQ8tJCqCG1IIVpdhbCmVRH1njW+I7zxd/Kx4jL97BUzMnoQFG/VbmtdbArFKorcXWbA4UVhAxelokRk2Y39xrQUpx5XtvomztFwEIvL41923HIZayz006hG3bqFzi41jB5EwwyNbJyQUV8E7MAKumG5hOdMD3ddOkw+MnZBjEzSCG4+bY1Xx+bDqIdY+ezvzqIa9TD4RyUgToCmLx5CBziAuBsiziMyvBo5H0lnIqz6A80sBv52nYugfTifOBxvqckZ8xDcI+g023bEL9eAN3e18HuiOVCjwz5+asIyc/9DmSbsH6WrmbQiPQ3WBfqRQYMUi1o5TC1txBRgAsPUCFvYYTbTGmjPHIx/3atg0i3QOJXfdGzDJFxFvK1qnzMhHu/7thOfd1xEecj89659tITA8R7D4tKschEGvHa6cGn1EIIUZB8weY4g+xvzvXls/p+2A3GPD1eW0i+rdPVWd+7+9q+LfPnOau615kXvsbeK0cVLyNR17ZyM1XrRxfvbDSKWuqtqFXVWFQnPP2G2eu5PmXtnG8oBBQPP7ar5g+44MwbeyKoGw7Z6A0AO8EHGthPJMAqxBCiGFKX6hzA6pnTFe5bRv43cCTjo+ABWZ3IvbAeW5JaeTlkxdrz7TW0pIRWlo7M+/bdftpXb/Q7SbcTz0lkIoxs+UYC5uPMGnGp3uW6/Hi1RTJdML57gBr/2OKji1H11iYqMDR3cpnpPspuq2pTJfggH+CVt4NE0Olugedcj/UiLc7iKSBQqPFGyTkC5H5QJUC08PSitncdtsMYHiBL83nx1A9AdZIMkIsnsSynfF1AyCGJXVoPwCvTVrGoZK5pO9T9pU4KHR8Vgcdd91Dl3d69/lBQzNN8nzucadteopHbvfxgagfTdeo62hmRThvoNWdM7rSCKSiRLzgt7rTnNRDQTCfd77763z2rndnTnV21uHrSzm8dqSBKYUzzml506fQ9sAUYmZPQHiqFeClzbu5qbZmRMt1Dh0kOWshQdtt7f5quc1LCyI88fb152zAMSHExUfz+fGpKFAIgKH6CbAGx/9ATmNhoLrWpls2UUstKEXVqyfQVHdXMSAWi9DUlRy3g12FkgozEc4ZVLZy+hSCTzcSsNyHrOp0C/VN7Tiqcux68dk2KcMDRvd91Zq1eKQOek5JgFUIIcTwpLvBZgdUh5IeIBLBUxrmU4fCNIfzKUxCnuNWAPye83s5qtndyvOXux1NlzUAmk3Hz3+J9eV/w3zuOXYsriBh5HbVunr/43R5wzw7PcFbd7UA4Lc1jOLSzDSaYeLXHJLdg0ylA6yDSSfBjySHnwc0WzLsI2Xk5ihc2uAGUtzCaQRC56AV2vnQHVQOpBqByTlvORrgNXlKKa73ZLWjVhDymTz1nidGtk7TBKfnM7MdG2yL9rhFSejM3fyyu4lnbjZqazNdpKmu7nf0V3F2WadPEDN9nMyfk/O66h4nNuHJZ1fBdCJ2z3fJTRHQ89AoZjjEutz8x+ERpJ84G3SlkZfs7PN6nhVH83rBSeK1EoD7ECY7T+u3n7iLDUs+j+8MA2eMpfQlJu4pyLw2t8V9Z/dPf8wb1y4iMIJghGPoJMwwdDc2T2oOFp3A+LxpF0JMDLo/gNeJ9X1oH3brJVrNCgIXSQvWwaTrQzfHr6DqsU4gHwCvo1HfFh2XAdZIMkL0+F7KzYWZ1zSlmDx/LoFUS89raMRPHieSmEO+39PfooZNObktWDVNP2MOVjG2ZG8LIYQYHk0Dr5e+LVgHj7Im8gN48vIw0Ih6c7vN+MdBoC8ca8v529Ggbd9BsG3aCitz3ksacfxWJ0saThFMtbhPqJXC19iO5u2pEGumiT9r6G3l2MRTA4wYWlvLb37QyV8fCvHuQyFWHxvdyKK2z8QyQjmBkd6C42C/nw1a95P7G3c19dfgmAPFUFfp4YVpywmnNMIpjYJr3kxxcOT5zjRNA6+OYbsDBykUKpWiPTbyrsa1VdvYlh9le3EyZ/RZce6ov/5rTofLUFmtSwJW7jQnbG/PIElKgenmYE0Le8Nuy/1x9BHqAJZFKHEK6G6lqmnM+fu/RPO5N6zT2o5m0gNkC6Y0jrfFzn4hIxE3pYltu98nNBY0uwHWmOlegdwfjRMnTo9oFY6pkzTz2F/i5mBtLvDgNVPnJW2DEOLiofn8eFXueTQSa8fuaMPuaIOtW857767x5JH3/pHJN15Lh1+jw6/hODb1ze1nb4VFRfDsc+7PMJXG4Iaj5TiaB1t3r6/tWpLyimIOelownGTmArZ5y29pi8TPuMwhsyyS2TlYdU1asJ5jsreFEEKMiEZuE9YzpQhIFrg5CptDc2gMV+DoZHKaBvPOf5fZ7/7VTDQVdes8yg1ntQXzsH0eIsGpaLgNDYIpMG33CXR9GB6pbGdPrJ49sXp89HoCbXrwa1kRzkFasCoU4YJiquP5VMXzubqrFJVM9DvtUChdx9JzA6jh7JSghkE4PPpRuMel7gBrINW30hpKuq3xdAXtfoOI100foHk8Q2ppOpiv3HgHnqxWrE5XJ+3xIQZYt27r+ek2s0PhLynBV1BApP4QyrIGmlucJcrQaQ/kYzg9DY2ipvt7PGAS95t0eLO+R5qGbnoIZQVYWz/ZimaaaKbZk9/3PNM2PYVjWZREdjC54zQF8Q5qTrzOgop89EAIUMxv3E1pVwMoN6ic/gklFac7xvCGcAiUBl3efLfrYy/+uMW+27/ek/9uGBxDJ2m6LcaClsb7Vr+fze9/dtTlFUKIwWgeD5qWQFNZdcKshgpaW4cEWLNoXh/lPg1DNzI/p1s6UUMZ/2GENpdbbC63eO7o0IOsCkU1ZTQGc3u9hLraCHlNfj/fwGtl1RNti/b20fVYy1m/beXmYNV0vDLI1TklKQKEEEIMi6Zpbh0wu1Kj4FNPfArTdwDom0+p9t5aHvJodHrDHCxZzrrj6S6nCkwoLingfPveX/yI//3YpzOD1Si/ojVYQMGMSo4WTc1pteZLuaNbJRMxdgcGHkxJM00C2bkU1MAB1pTmoJsmtmbQFpyJrZs07d5NWVX1iLbHMTQsI5STPzGb4djMKMsf0bLHO6376X2ge5T0UNINLve3L9ItQzWPZ1QtWAHMyqmYdid4ytxlJxIjbsFqt7WwKB7mZ0uKSJph8hINfPTQPrzzFo+qjGJ4HEPnVF4401oyfQwprTsNBBAxIJw+PxgGoYC3Tz61225bn/l9vLSNXNzuZWpnF1fWPQ+Az9YwwvloQTdgPLXT4rZNz3H/MoP6vHkcKXaPPS0eobGpHWaXDrjs0VKOQ6IggO33YEbigKLLW0Kon7iuBhyLK5I7X2M432CVShH1+qk+3R20LSgA00NBYGy6agohxGASHg0zESVl5LH8RO4DVM1xzs2AqBeQioLcRgPRrhidCWvMutf3oWA4n0DtnOeotHxM04uZ3DWJYCt0ed3r2aQTuwA48elmPvrv/0gsfeugoK1z4PuIYUvnYE3TJUXAuSYBViGEEMP22GWVtCcVXV6tu/LRk3i+P4ajULrG4eKZfaYr6WpjdmXhWSvrUOn+AHmJDrq8FW7wt62Nb18S5l93TMqdUCn+L38Pf9/UjOPYtH2/0M2P2Z/uHKyZWQdpwWofroMSOFmwgvbANNDgV3uauGlOgtLw8PJwKaVQuo6t97SsszXwp+KAgcexWXvy9ZxuzBNKdwvW4q6+3ce6vD0DfuW0ezA9lOWNLt+ZHs7DY0fclsIKntu6kZ/t+gHPfeXhM88c6WnBoGo3ED1xkIYNKzhaOB1bA11Z/NtDn+a7n/3VqMoohk4phTJ04mZPjjfdgWltxziWV0iXN+sBRTqgGg4T7if1xnjscu6dvYCUtpf0OVlzHMxJUwFFSu85pyssvFZrzrxNrR0opdzUGGdBcv8uEsV53FulAB/KfwLL6Hm4YGuwvTtzy4pT8MIkuL+iEOPe2iHvayfWRYcvnPOax+8j5JWghhDi7OvyaBjRLpJGGEeDw0UwpztFpy+pCEqANUdBfhjDSWLrbiVOJZOc7kiclQCrHY3w8UNl5DkmL0zXUY6Dpg8SqKytZdtai0s6A8yMTcHp7ih+oBiOmlEev7qcv8JtpKLpcTJ5vjWNjq6x6xHSNwerhsc4O9dp0b8JemclhBDibHJvvXO75bz3l4eoPrbN/eOW3Om9FtiaxmNzZ/VZ1tWxI+MiP5DmD1AU7eR0d7YCpWnk6WW0+9ztjHVfMVOxGF1aE46T1fV/gMGHNE8/KQIS/XfzdgydiDdAff40ljW4lbKnXnqE/235A8+8/xsY+tArSCqZAA38dhANWNao8eIUxaWHX2FKexMmCo+auBX3dCXYUA5zmw5xsNg97gwnybS2enaWT+8zT0HIT4F/dNUiLRDEtCOkPG4+x5SmccLvG1IgSmlQ4A0xPenj9Ol9HKiYx/SO6RwtdN93dJPK/dPYW72cKa2nCcxehLHpqVGVVwxOJRPg85Iw3cB7xAtoMClyimDSpjmUTm3S89lqQP4FkttY3/QUv/1nH+9r9YKuE2pLoHncG9XnC7uoD7qpAq55tZNJcxfzieLu0ZtNSERjdMSts9baM3X4AOga6ccgWmUl8WgB0e6sKUpz97Wpm2jYhBwDFVzGtvqhB7JVLJoTYNWAwpD/rAWNhRAiW9Srub1eqEABVae1TB5vf2FQunb3YuTl47NaiHrdp2sqmeR0Z5x55eEzzDl8yaIweY5bJ5zb5cU6dgjPe/6hZ4IB6v0ldjEnC1cyvwkcHZaftHnniWau+8/HM9P8bdVf8ExDz71A+xgGWN0WrNk5WCVFwLkme1sIIcTYGOSm1G8pmoPFLG3wEMzqMX3j7icpSI2PvJKaP0B+ooOd5W4rx06vImmGaQ65A3KFkpAXtbn86Al6D/A14DINE7+WO20s2X+XccfUOV4wLfO34YBP6ZTVdbL9WEu/8wxEJeIkdRNH9/BahcZr8wpwdI1QMoqOQgOM6TOHtcwLitETPF5xYgc1J1+nNHKAdz2xkXmNB7A1twUcgKNrOLrG0sn5ow6s6IEgHrunJapHaRjKS1ey/1bLaSqVpLarmEWxIG85MJnH5tfyzKxldPbq7xxKwn9suJxvXjGDL1TsOau5x4T7PQIyAdY0n5XAn2rrma7X+SA8ykD9uRTVHTytETzNHRhZresPFRu0NdXT1nSaVDxG+AufI+m1MzlkVSJOayw50GJHzf7mf4Dj8MY6uLYONjxzlLiZhwZU1YOWssiPHcd2bGxNoTSY7UxmTVvfhycDcWJROnwh93xhGLB2naQHEEKcM11emNTZnsnxbWfVF/NTI8/BP1Fp4Tz8Vk99WCUTNHSenf2UzHfThZ0OQdJOcsf3/3bQ6RWKqzoKMfyrADe4GvFCO0lmP/pAzrQFvdJRdcaSZxwoeKiUZdHi1YgYtjvOhaZJioBzTPa2EEKIYVMaQFbLTEXOKNu9hZKKlmBRzmv5iXbCiVa8v/nD2SnkMGk+P3kqia272+foGkrTaQwVZ4KurT7F7LIwRn4BAUsjYGkDpwcAtxVCdgtWIJboP8CqDB2T4sxAVLYGlgYFtof/+u/PEk8MPZihEnE3cNDN0dyn6EXxaObCb6oJXAXQewKsHsdmWf1eprTvIBRrZeHew/hTTYDbxbhgzeVUrFvPwsnFo19tfgFBw0J3ej7jQtugqWvwG4DU4QMU2R7iZjkvTX8Dbf4iAhbUFXcPTKZBXrLn+3W0eA3NxbUkGupHXWYxMJWIY2saXtuLBjgoUl0R7IYT+FM96SeCKdxWR0qhKe2CSr3xyu71HCjROFCisbUy9z2luT8xv4FRUISWambJSYslJy3Upj/RGh1ZfuGh0OsOAfDoAi8PLi3jV/NCLGv2E3ZMDpZoJCaVE07tx0xfd7qT5FbEF3Lz96+g9t4zD3ilorkpAjTTM+pW7EIIMVRdHg2/1c6yBsiPuyloALRwmMpYx/kt3Dikh/J45/bTLD1ls/SUjUol+OrPPsPq93tZ/f6zU6ft8k7iRMHVHDFXU+/PSgtUW8tVtxZx1a1F1N5bS1K3mZXIZ05rBVX1UJDQKLBM5n/0/2N2ae6AsvnB3Ie2dipF1wC924ZLWSlSuuRgPZ9kbwshhBg2BWg5T1tVJt9Qf0IpRUuwMPO3ZjuUnjqO51gDenB8jGSvaRpeO4nHjuW87uiwrAHCCUVlqIKyeCetn2xl1QmHVSecAbsJgXvD3rsFazJpYTu5r6lUCqVrdHYHRUPdsVRDuT8ey+SZl15zR8lO/wxCxeO0BgqoajSoajRYMXUNgZYG/B1RzK4EwZMtmH96eqi75oKjaRpOP/F+pRSaZbPk9GsUJlKYuomhwaX+Lrzh0R+HmmESLJ1EICv4Vpry0Nw1eHDcamrg73Z7acyr7vdBRVU9zGlRxMxMHImIv5Stu4+MusxiYCqZJG763Nb5mobP1sBR+KIR3ra7Ha37XzYNLqgAK9DTojuc283S1E1M3eSy6esxCorw2B3UFbuB/1fzOmk5w4OD0VC6RnOgkD2VN3K45DIOlF+T837KcIhozbx514HuARNdcU8Z//y/x/nCF54743nSjnbREugZYFHzyABXQohzJ+rVePOeTvRejRf1yzdQ9ZP/OT+FGsf0cD6FsTZ05Z70lWUxKxXkjYmFXNNVgt3eNibrSfcOsjWTxryVpIw8Jp/Q+JOTh/P887BtG+9ZsItrTvu5psHPlE2vkjq4nzcdKCU/oWE6uA9cHcWblk/F7JW7NRD048m6N1CWRXt8bAKsVsrC0XrWp+n6uEjDdjGRvS2EEGKEetUIB+ldHUoqWrNuZNtMizanniPF4ysP6NS8KQSsAVoNKEVpPELQHkarLcPA16sFq1IOcSu3y7gT63KDZsG8AXfjvqONHD9Rz+0Vr/GJyu288Z4NA65WJeI0ZwcODJP8SBOB+jaCJ1vwnMXAyLimYFeRjem08IGX/sA/vvAUl93/70w1U2COTWDl07f8iHCiLfN32DFoigy+v522FvaWzGRhcxhD9XyVljXAilMpVh5/hUOF3aPYdwfDYn6D7Sc76Yi7x6NdewXRq9dz2+3r+ZvvXD4m23LRsy3iWZH6zoCOphQeK4HXtvCrOFWne1odAWjXXkveBRRg3Vq/DdX9L5LsSW9RXVnNbbet57bb1sOmTej5hfiy0l/gOLS0d52VMinbRuk6m6cu6/f9qkaDW1b/HR1Bk9oDOwik3IdiMY/73ThYsYRA3IZt2wZdT2ckSpenJ1+uZnooCY1uoDshhBiqh//+MQwcJnWcynm9elKYvLMwcNOFTg+H8acsiqJt6I5i8/6nwTDYV345J0sv57N3/MXYrCiVBL+PmvpSvLaXUEKhbIsOw8sb/7aQ978pxYKOEKcK13C45CoKgm/icPlUmkIlOYuZ0nq634d2uj9Avt5zH6BsO1OXG61k7zRkuiEtWM+xC6cGKIQQYtxwBxjJCrAqhdIGvoAHDhwl4l2K0z2JYSvKOtqoiYx9YvrR0JRGINmCpirQHZUT7NR1g9lf+XeYWjj05WkaftNA6x63AADHIZ5yCGWlYFLxOEnDg60ZoNlkB6/TI94/u/v3LJoynXftawc8RPfuQr23/8GTVDxGqycEdncFzjQpIqsVpXnxXP6bA6CjkfJoWJob4E5pCo+ToizagELDrJgyZgPbmJOmctXBNu7pzjigK43G+kZYVNnv9EopnM4O9hRMw3B6WqgaDsw8dZR1gRT17cco7/QCVZn5bMcmFY+z82QHb3jfX/Ctsr005Plobq3C6irhpjv+noc/9KNhDY4mcqlUipjHn/k77M/nx+vauWNdIZ9qn4U32Q74sXUwlebm8fR4L7gWrO9/i3uMHJoZpnWAabRgiGnhMPuznhW0tndhO2psjrGs1qbOrzdio3Eqr6znfQUR3W3hE3ZMguEAp30WDy3RmdO0n22VyzOTNhVNp710Os6JzgFbkhR9vYjrGuexxlmD4ea8wef3UhyUoIYQ4tzQfX5mffJr2P9xO8/PDNPpy6Ois56aGSVnnvkipBkmU+IeDhQ20xwqIuKBcHc8UemTiFhTSUajeIPBUa1HJd36cmsgK7WZgqYCDzODK8CfYGtoCeFkTx391wvLQalMmi+AyS39p3HS/QHydIfmdIzVsuiIDT/AWvR1t3zVldVsusXtTZdM9Q6w6nhMqQeeSxdWDVAIIcT40Sshe8xK5LSAyqYnyIxqqQDlKILJCFSvOtulHBZDaQRTLeztrtvOb+55XS8o7JNHaSh0rwcfing6XOs4xFO5LVhVKkE0qyVVxHTY5etifcspIt75mddPFFRQEDvEnvKFJDwmZXWHmTp3Vp912vEYbVm5ojTTZO0jT6BXFPSZdqIriYFPaXgNL4auo5TGPn8M6KmAm9P77sOR0gJBCqM9YSoNaDt2nHhqMX5PVovt7oCSQtH+3nfTHi5CYWUeQlz1+jMs3rMb1l/K8VSSkq46rj1wlP+8/FISZjEKxdajL5NXv4QaHQzNw4nCN5Aw3YM3v6OQw6dbmTNp9LllL1bKSrkpAro5moaDO/BV0oBJXpNwku4u6gpsG7/fd0F1x6vZ3cqhrJu0tPTNWpqmaXxg3Xv46NPPZV6zE3Fao0lKw2Pb6lNFOjPpUvqjKUXQ5yUweyHR/bvZVXoIQy0E3DvdAyVw8vL1bGt/nlubGjBLy/ssY+6RCKVWgJjptjIHKA77x+xBixBCDIV/+UoCVpyr9z9OSvfgdVL4SsvOPONFypw0lWCyheYQme4+6bq61qbxwutH2bB64ajWoVLdAdagW3+Kedx0YYbHQ3tgLssaoLXXJUqR2yjDSKSYWlLY7/I1n5+87BasVoqOYaYI2L60jOsv07FQPB95KfN6stf9hWEYfVIUiLNLAqxCCCGGze0127sFq8at17oX9oJ7azM36Mq2MI0wdNcdDAVPXzqZTW9azVPv+dO5LfgZmPc9SPwLV2Iohd19o52+AZ99y1+RP4IuW5ph4tMd4nb36NuO3SdFgEqliHa3lKtqNOj0aPzT5iRXHjvEp9/iBlhtx6bNn88f51/LvhKY1wI/+cJ/8nfNp5j6f4/kLC8WjZP09rS800yToqAHej/Zvsh87rNvwNE1NEcRfeEVTI8XJ5XCO2/xmK1D0zRCXS1oykZphvtAIR6nJZpkckEgZ9raqm3c8as4HXc7MGk5we5RZztSLTR17ERLud+x6GyTazb7USgWNLzCtqnXoeHeW8S7ujiYV8zeUpOEWULVaXfZhmrj5P5DEmAdDcsibngzD5PWz7iM45F2PIFqEodTRNoOUqKy9q9SFIQDAyxs/Gr95EDtVnP58/KwPHFSRsgdYOQ3v6bprm9R+qsHx7Q8TiJORyAPcK8yMQ8ErO7c390vFgQ8WIbG48FGylSAmZH9tJcsAc39DnV5i7imbiaJv3obpu3pmyvbskHLI2CBjYWhNArzR9fqSQghhss7bzFWLIbH78frpLATCYz8wvNdrHHL86OfEvpULWtPJHhylo+IF/Z1N4owdSg82cayriQl2d3EhikdYNVwGyqkByHbUQlLu3/PS8KcFjINMrJvifSURbCzi8o/PkJ/tF4pArAt2r/2DTi62f170yacWBQVj6HnF6IZuenU7LYWfD4v0xPuSssafKhkAs3rI5WyqZvV05hikmd8pWK7GEg4WwghxLDZmpaTIkA5DugmV7YXckV7AYFXd2Tec6JdKHJvXC0zgdZr8KfxwCitQMfCZ/VtiTt/5qSRLdQ08WVva3eKgBypJNGslnIFG66n+Bv/yaQplRiO2ydXd/rZX4bJpooFJHoFbNu64rmTeTwXXLfl0UoPGtEc1MDrhcrKzMBXStdIxKN0dbaRiHWNeas1u6sLXUWwdbB12HZyM6395WGNRNA0jdNBt4Wd4cArlQ7/VXOMq/+h5/NSmsZ7r47y3qtjHCzo5Mq6BmpOKZbubyd5YA+vF0ymIzCbcIpM4FVTEL/zzjHdrouNsnJTBGi6gaa5DykSJgSsFrx27ndv3rSJ2/JID+fjtXsGcFNAk290aV5q/8FD7T942LrnKTdn6rZtqHiM9nDRIKlMFBV57vnS0hSfeizKX289iOn0fBaGbvB6xVJ+NHcDu/Mr+ixhuuWnLZSfSckBUJw/vlLWCCEuDtPrWkm0tJBobSXW2nK+izOumVOm0250cfnBV/HaKdLV65gJnbrFE3v+j9dPDTCWwhCpVBJb04kGw2jAgaznqBrQ5e0zCgUAdSUa2ydp/GlNGfN/eMeALUc1n5+8rPEZlG3z7WltrF70HKsXPUeybi/ffs88vvfPVfz7305CWbmtW5N/8VYmdUKHr4L6vGri3gXETxx13+s1reciSgk2XsgeF0IIMWyWDjnVC9vGFwwz1XKDEXnNPRd4FYthGaFMC9ZlpxU3ve9W3rJs8rkr8BBpHg9JO0nIaiTqycu8biibqeUj616vmR78zzwJoe7Ay9QZfVMEJJM5gRwMg6DXwPifO7n+k5+mKTgjp+tR1AOBFIBNR6KN/Q0Rlk7OGkQsluzJvwoUBLzoF3nX11uqb+FD735/5u9XP3P2njEva/cTSHaQNNzPJJKM0NrRBVMKeybatg1m2jhmiPr8nqBcwDFYOmkShrk+81rYG+aep9zfv3qDyYqTR/jh6jL3IUfd81QFCrnkRDsohZ21WQldqnmjoVK5KQLQNf7j2i9xyawSYptf5It3v5sl9fvYOWkRAJXtjSzO/ownGD0cxme10eV1z90KOBUY2XkxW8Swc25WVSJOR3fg1uznLlZTMLXQbSn8uwfc81oiECWYOEBHcAEAluM2ee2MtfMsBZRGEpR1pzJQtsW6aBGbS/JylltUKAFWIcS5p2sadsJ9CGtc5HW1M9EMgxdDHXym8Th3/F8LLcFCfrh6GkcLZwKQam/hUHOE9XNKRlzvVckknd4gCo35zT2tVCPdgVXVPdjo0fw28hIxGsKTsHWY2aowlMak2jexYmrRgMvXfL1asAJGShH2FJPQInz2W9cxRSmO5IeAIP/8X7X84OPPZqa1dIf9JdNpyF/pXjs1eHLXKW6YOZdkyqaqoidfv1dasJ5z57Tmfdddd/H444/z3e9+l/LyvvmQ0tra2vjFL37Bli1baG9vp7y8nGuuuYbrr78e/SzkkHjttdfYuHEj+/fvx3Ec5s+fz7ve9S7mzp074DxKKZ588kkef/xxjh8/jt/vZ8WKFbzrXe+iuFi64wkhJjZbzx3kyol04FU9FZl8x4NKJdE8XlQ8xuXHQrzWfdrXHEWeb/wOJLI/GOeShjqOFs3E6b7mzGs4itccYSXFMPA5PV3zleP0kyIgmZODVUsHWEOFzGw5yrGCGaAg2CtFk7tPDR596Js8fuvn0a68ErZt4763r2a65qYWMJRG4RjnSLxQpLqbsdZ7k/QeYurjX7ki83uvzsOjpjkK0+ns/sP9r7UjmjONAlRekPqpc3C0nmPritVv5703rc/J1/pvn6rO/B47sItJ7fU4mpvxK6ZbbA9HmBNVhFMa2Q8+ksb4/Z5dEKwUCTOrm6FhEOj+XIx8N7BYfWo3kzsbSBleZjbXT+hcZ3peIdN9BklvGMNpx+nqpKUjRkNngvK8kZ1jIoZNU75BKt/Dq2YnqyJ5qHiM40EfEd1iTgvsK9VQWTm/KzuaMA3dTUNzi/vabV+9jMKDu5jbNod9pSYxM93qSLGv2MO8Yy3ULnJ7IdgN9aSMII7W8/3QbZuicNZDLiGEOEfybIMVp7vrhY4z+MQC3R8kcKwJv6FT4j2Nuc4GZlJVD6ARu+dH1C/8dJ+0TEOlkonMQ76wY4LmVr4NO4ZGgFCyiw/9/tfoiS6evHwOjaFywK0baCiumpVPwDvwPYPm8+HXwaMpUt33TnMSAfJjlQRSjRTGUxwpWsuJginu9jZHiCYsgj4TlUphHTvKlqXX5SzzcGOE050JUpYNz7+ced1bvWxE+0CM3DmrBW7cuJHHH3/8jNM1Njby6U9/mscff5zm5mZs2+bkyZPce++9/M///M+Yl+vJJ5/ki1/8Ijt27CAWi5FMJtm5cye33XYb27Zt63cepRTf+973+OEPf8ihQ4dIpVJEIhGefvppPvvZz9LU1DTm5RRCiPHE0rWcQa5UKgVa1iVF13AiboDJiUeJZAUPUYo8//htWfdcuB1ddVLZ9gQLT7/OiuOvcum+zSNenmaY+Oys3KdK9UkRoJJJujSP2+rUtsEwCPtM9IIiyqPNBJNRghbo3bPZ3UG7uS3u71OOtjDl65dllmeZWS2zlKIgdPEFDj51xWfxKR2fOvcBL626GqXaM8FV27FpjcTcP2probaWuE9jbSSfveWLcuatKAznDoaFO+BQ+ic4exGBaITC6FHmN7stmS27/8ERErq0XBiN3oNcabqe+Wz0gmKK25NoQEWkmWntp/D8/rHzVNJzwygs5v3LbuKSEwlQis1lFs/NNVn73WtHvMwpKT9/2VzGi1WFPHdJOe++MY6Kx+j0ZrUm7TWg4pymo32W0+5309bcuOf3eKyshxnKfeTwgx/dlgnSWqeO0+lzg61dXvcnmEhedGlUhBDjRDgMBQXujzijTdur8aDhtRVG0qbVaaS0qw1DQV0RvJzfzrsevHXkK0iliHh7UpslNIey5pM0JB/ifyp+SNVLP0bXHPAH+KcbPsak+GYqOpso72xi3eEXmVxWOOjiNU1D9/vJ03PvBVKGO3JWxDeD9sAU7O6Wsovrw7x4y79AbS12eyvNgQLipg9DueNaAGw5/AI7T7SRtHOX6fPIde1cOyd3Hb/97W958MEzJ8C3LIuvfvWrNDc3k5eXx8c//nEefPBB7rjjDmbOnMkzzzzDCy+8MGbl2r17N3fddRdKKZYvX853vvMdHnjgAT70oQ+haRp33nknXV1dfeZ79NFHeeaZZwC48cYb+dGPfsS9997LW97yFpqbm/nhD384ZmUUQojxqHcLVmWlUL264jhRN4+p09FOJHtEaEeN6xvZDh/8JtyA7nQwv2kvU1uPsN9pHvHyNMPoFWB1+uRMdVuw+t1AglJouk7Ya6IHQ6iUxaKGHZm9HU5phBOHMZ2soJqC9Y3L+HLpTA6XTiHuKc0qgEZR/sAjck9U/tWX5vztXbA05+/soOWY27SJypIStO5/hm7QFUuSsGxWz3+a1fOe5shkP367gg5/YU4eyEUzB+7hA/Cnv3+KvVo7qvN5prYfI+KFqtMQSrkPgA3dxMgvxMgvJHH9DWO/bRcRZVnEs1uw6gZ+j1t1NkrKSPbKI22UDP7ZXei0cB5fffFrTDu43U1FocHCZo0PvVrpPjgYJqUU67rcPKjHC6p5bdJbyC94J4dbukiYuS1iA8mdTO44ypLT21h4sq7Psu665REqIxpeK8X8hse4ade+nCw2QctPa3sEpRTJwweI+ioIpNyHVrrtMKv+2JjnYhZCiCGpru75EWe2aRNccQVccQVaKsWRQIqC2PHM28q2sRoMOuMjG9hVpRI5absui5RQUVpJux0nXhzibf9cSM3uVmp2t6IXFjOl8TiXH3qGyw89w+TOU+gFA6cHSNN8fvLSaQK2b0cD6vNXsL/8Jk4U1mQaUgCEk7BTGezfvxenvZUThe4DQoU7AFdVPcw9GuHQiUYiTm54z+ORnkzn2lkNsCYSCe644w5+8pOfDKnb/BNPPMHx48fRNI2PfexjrF69Gl3XmTx5Mh/96EcxDIMf//jHOGPUdP4nP/kJjuMwbdo0PvGJT1BZWYlhGKxfv56bb76Z1tZWHn300Zx52tra2LhxIwBXXnklf/d3f0c4HMbv9/M3f/M3LF26lG3btrFly5YxKaMQQoxHfXKwJhPkJzU0BdcfgBv2KtTf/i0AqZYmOvw9rZE02yZ/HLdgBfjiEw4v6Y1E21roaGnASvUzQNFQGQa+xnqIRNwfxyFh9WrBmkrS5Q3kzBPq7l70nb+Zx6TOk1y39ymqT77O5XUvsk97mhktO8jKykBAGeybGuKBSzb06Rp+MeYW9EyfQ0lznKIuRUlbqk+A9WxTRgzIHsTA4uaPLWZlNJ/Lo27le3lDKeFkz8BUBakYC+ZMOeOyL5u+nh3BTqr3PEVBbC/gtnJwdODSS6GqCqqqSNjjbyC5C8qXvpSbIkDX8XenCtEMgxtfbM0Ex814CqNk4g5wBW6rm2aPzeSO+pzXI3ll/Q4kdUapJEHHIOqdREtoNo5mkjDzeOKxLZn9qnAf5hXEDrDyxKvMbjmIJ9W3xbZRXMoJr3ue1lAsOf0aHjvWU3bgxPHTqHiMH/zyU0Q9RW5rp2Io+bsPMv3/Hh5++YUQQpwfmza5P8DeYo2C2FFCSQdDuYOcVia8bD14ekSLVqkUXdnjIlx1FaGvfJEDM/rWpf3Va/E4PZVxx7LRA8E+0/Wm3/Ed8p/4PWzfDkDVaQ1DgULPPMBM/7xWDi/M0Lntr27gI9++kePFU0DTCCd7BnQFsCIRDqRyH076xnFKtonqrAZYf/GLX/DCCy8wb948vvrVr55x+nQKgRUrVrBoUa8ucxUVrF69mra2Nvbu3Tvqsh08eJC6OvcJ+Dvf+U68Xm/O+2984xvxer28/PLLOa8/++yzxONxDMPgr/7qr/os98YbbwToM58QQkwklp6b61E5Do6mUx+GpOHmvlTdrbsiHRFs00vMdEf5bPMoCgPj94K/fvp6CvwFJA2FlUygRvlQTzNMfjcjyvYym+1lNspRfbrwpJJJt8WWprk/ukGou5Xv9/71MZTtUBptYVHjXqY2HuGFb3WwoPEwc5sOEPMAmvsEe0EzhJLu/7rj/hiWTWHemSt7E43m8eA91YL/SD3e440Yeee2691nrvwcARXD6O6mr+IxFkXKmJMKUmn5KI5BS9B9+JwOJM0xbIyh5PDctIlNd6dY3qCzR3uBm7f8imtff4Ibdv4xZ7KU5WA7EmQdqaSmY6UHCtM0NF3P5GAFSCbihI40EDjRTLDTuihaQO4PJZnU0Ywnu1W+pvFc2dxhtxZy4jFu2amRMN0W9xGv+7M9HMm+t8RjRTM9JhzHwUj2nxLjhWA70eYmap8/jdYZJZRsJOaBmAc6PYoTjW2oaBeO8pMyvDiaW3YtEKD8Is1TLYQYB9LBwk1noUfNReDo/9fMRz/xEJWdh7F1t5edqTT27z+Wk797qFQyQczsCbC64yKYVFdWZ37SzPJKXvd1UBpVlHY6zDscGdI6tMOHKTxyALujDdXeBkoRTrqtVec3wYImctqxANi6jy7fWpq9eZnUOfuK3TQByrZQv/8dvat8fl9ujEucfWe1CZGmafzlX/4lN910E4YxeB6wSCTCsWPHALjkkkv6nWbFihW89NJLbN26tU8Adrh27doFgNfrZeXKlX3ez8vLY+7cuezatYuTJ08yebI7Yuru3bsBWLx4MYWFhX3mW758OYZhDJi/VQghJgJbB005LK9XaJoNLdtR5AYXFG7gtTXqtipyB+CBvLzynCDFePRvn6pm59HnUPR/Iz8shonh5KYISPZqwdrVKzChGQYhr3uJ1gNBujraMKJ+NMfBaI2gaRrzn9tHweqF/HpBIdCTEiDdOyj9/yzDwTfSAboucBrQp7Z5rtYdCOK1Okga+QDYLU20BQu5vm4yUU+QmHmKlmAxluYOXqZHE0y1Ooe3jupqXtgBtddtg5q5TG0xWJ09gVIkLWfQwRbEwOKmx33gAWAYOSkCMtNgg2XjX/OG81DCc++1vAT+VoeDBYeJeOZRVwSg2KY3srIxwoppZ+4amabicQASZiEGPbmlI4aduV5gGDRo9QR0B8eGeKQdTfWfUzrsCzO/vg2A/cXtdDqngOmZ91u7kjhdEWKenjJaOAQ85rhOWyOEEGJwvuUrmX3ydWKLZwPgODaxSIT2WIrC4PCCjCrZnbYrTXcHnh0opdRebxfH293AaplROKR1aLYiYrSxrRKq63NboqYZCpaehsNZi4x5SgG7u27S/eCx+3LZnuqkdxtbn1eubefaWd3jf/mXf4lpDm0V9fU93Y3mz5/f7zTTp7uVpJMnT466bOn1zZ49e8AyTp8+vU+ANT3fvHnz+p3HNE2mTJnC0aNHiUajBIMXX6shIcTE9/Xrvsmzn/oYr5VoGJoBPlCaG9Bq9+XTlFdBiebnH++5kvf+NowdmukmYtc0ipLRC6KlV9gbJmC1jX5BhoFhJ3oGanEc4paDUiqzH7oSuYFcr9eD1+wJ5Fy6P07tvW6Ow3QFTw+F8Te3M7XtGZpCi4DcB48xE0KJDi67687Rb8OF6gwPd88mPRjEZ7cTYWrmtU7/TF6ePpN9xXCoeHkmoNSl2zhGikm/fGh4K+lu7ZKu8kfr9nH3H7f3vO+4+X4lwDoysV6pNgzDwGvkBlj3lbof4qqLpeWRBk57Bw3OqxhaT1240PGw72QrNVMLh3x+V/EYCkia+QTSp0ANltQrbA2M7udQp7R6juun+dt68CqA/gOsm+5ohe3uefKj74GFJ8NEWnve74glsbs6iXkLmd+dVjs/ZVAa9l0Q1yQhhBD90zQNvx3HcOLYunuNUPEYDZHEsAOsTjJJNGtwXq174NmxpM+eQ7jzIJpyMLpbRGi4QVW/laC0qwnHeQ1QKK7G1kyqTgNYOS1bU0YMhVtWW+WO76Ap1WfQVHH2ndUA61CDq+C2YAW38lpe3v8gAQXdI+s1NjaOumzp9U2aNGnAadLra2hoyLzW2em2LkkHXM9UzhkzZgy5TM3NZx5EpbCw8IytgYUQ4mzTTA/lgWI3uNrN0k3yEgU8N2sDKdPkhLcAb2IrDaGevISaUpTHh9dK71zLfkL96ne6B7QZxWjsmmFgOCki3fW7l/b/icumvRfLUXgM96Y+0ivAGu4nZ1J/T87Ntgg/KYtiUc837voTxyf5aa+YTkPhVPI7TlNw8gB5+T8ZcdnFyGmBEF67naWnuiu8Ldshv+90QQu0FEwPlYy6pbHH48HUFFZ3cl6lHBL22OStvxjFewVY/T4zJxAX81+c9TFH17jnoRg7ljzNT2uuANz7vZaT9TR1TaFsiN3tVSpBwvAStHzu/eIAMU7HamVvKUQ9ELTOEAjNeujQsvnPfODhBzJvWSmLyIf/jcSsIki6r2n5BZSGpQulEEJc6JZseh3Pl95P0pwEKJ6r+xPVbWuZX543rOUkk0ms7Hq/4bZgHUjMZwy7x5vH0TEchzccPU3c0xOPqux4DcPaR7sXzOmz0A8d4q179vLA8iVouGnAANA0qpsMXi3ZQ16iip0VOqCY+8rzPa1Y8/IkwHoejJs2w6mU2z0yGAyiD5B/LBRyR0FubW3t9/2RrC+9zMHW19bWlnnNsqwhz9fa2jqsAOsHPvCBM05z5513UlJSMuRlCiHEWWGa+Ozcbu1Jw0ND3kLKYgahhELFO0geSLKlzMfsVghYYFqKGV0t56nQw2fkj0HeTtNNEZB+4JyMRVAKEpaDx9BRStGVcnJaW4YDQ7zhN032fdf99cAst0oVbm8gcGz/6MstRkUP5eGzOwAIJRTE2wn7u7tyaT0pHMB98DAp1jb6lRoGvqwAa38Dqomh692CNSBd7aiurObQzG0AVHQ2Y9gR2gPuuefJV3/OZSsXDCPAmiJm+qiqh22V7mvhpNuCJ01Tiv/8fSuhhMaCJkVecOjn5GAwgK4sHM393JRl0eYNsKI+SJsfN//qW64Z1znBhRBCDI2eX8g0v5c93dUe27E51dAK84c3CGO0d55vffAAa3jtej7q3QZ096QYSlnR+O6yOO/ds42GfIsOXx6eVD0fenEfGuB3NMI/+h3vuHUSin0EknkcKJ7O3BY3yKqh8ITCGNP9FB7tBNxro7It0iE+7dLLCEqA9ZwbNzXFdFC192BT2dItYpPJ5IDTDHd9Pt/AlcD+1jfS+YQQYiLRTA8/qKijODULcC/rCV+A1uAUSrsHbn55ks265vkcLuppcaQ5ioJU9DyUeGRqdo/+gZ5mejC03G47f974HRp++Dg+q4Nbb/s9USe3VVY4MLQARV5XT5C7Juv1dDoB6Ok+Ls4tvaAQ24pwLNzOokS+mxKC7m7Pyk3hsLcETDSaJpXxd//676Nep2aaeLSs6JRySKQkwDoSyrZJ9G7B6s39+7bb1md+v1i+Z5tu2QS3uL+/vDiPULI5E2A1lUb9sZMsmz60hgAqlSTqDbgtcU7D0zN68slFvO53Jf+aNxF86r9HVFY9EMBjR0iYhe76rBSt3gBdenfXT8NA8/ol/6oQQkwAmmmi623guNcghaK5PUI8ZQ+rJWdXPDfA6vd5MAcZgDT7ujgcPzi8nF2t21jY8DJ60uK2Be38vfJimB6qWz0YeQV85OkUhtlK66WvEvUFUJQR8bodPqrf/mb+ePBZvE4ztt734aNmmpnxHMS5M272eDqwOlhagXRwM5FInLf1DWW+dPex4QZY77zzzHny+htYSwghzjnTxFBJDhR3/6naqYq4lZf0yM9eRyOc6un1GTMhmHQI/fEP56PE541nygzes9vDi9MtUrpJ1UmLuiI4UFKLhsMXfvIVVs66MWeeUHB0I1oPlIhfnDu6z09CV+Ql6oF8urwwp8UdIK7Lp6GyAqEB4yBFw8wR1q/uFqwZjkNSUgSMiLJSxI3czyTgzw2wXuzfM68ySdinCaTc3lqaZXO6qW3oC0iliHly86kaWYer0mBuWWigzAFnpPsDeJwuoo7bPXTzsZeZE7NI5XfX4W0bze+XG1AhhJggPl77/3jvH3+NSvdcSMRpjCSYVjS0cXGU49Bl5TaKCPnPQhqZTZvQgOQ0D4e6ezsdDkMi5jZCMROFgDseBECdP87Xnt/M/y1aR7u/kILORq5cX82NVz9FXcH9/HrHq90L7gkOGx5Pn4E5xdk3bmoUQ+n+n86bOpbra2kZuKtqen1K9dyshEIhmpubB52vq6urz3xDIV3/hRAXCs3jwXD6f9hl624+vjktfVPqFUY6L7rBRPS8fPSUje4kQHcvu4ZK7yed4/FiKqzcwE14lAFW0c22zzzNWdSlWRQlTgP9D965uMnm8hP7WfWRH47J+jTDxKf1RKiUUsRTw8sLJrpZlpuDNSvgF/BJrs5sNbtb6fqHmWhAzAOg+PGWn/POGy7NGaRvIMpKEQvmZdKjaNhkj97hsS1WzS5nd3euW1u3obp6yOXT/AEqvF7auz/DWCLCqbyy3Gm8PkI+6UIphBATgaeggKCKkTCKAFCJBK3RFNOKhja/SiWJObnXr9BQ03aNQM3cnp4whv4cjt5db+2+1qV70j16u85HEwne8Nr/kXJsUtEIocDPAaisKGXdoQ6i3iDhBKSDrEUh70V3zzUejJsAa2lpKeC2+mxra+u3pWZ7ezsAfn//o4eOZH3ZA1gNZX2lpaUcPXp02PMJIcREoofy0FDoTgpH75u/LuYBrZ9nTCXtF07+1bHkjSS6u6qGCJ+hc4Om1JBTBIjxrdW0mJdqJalbBCy3yhXzaFR2PMNbX29GoTM9aaIFhtay4owMA2/28LJKkeidS0wMSaYFa1aA1X82WrFc4I6Vp7j2uMOeEveG1PRrtMdTQ8rDqlJJYmbPdMrQyY8fZUFDB3FPgLlNByme/l+Z93dONrhs09BbDWv+AO+YfzVf27c981qXJwBWz3ciEPAN2vVTCCHEhUPPK8Brd5AwuwOsyQQt0SH2Kq6tBRTRSTNBL8y8HDpHdfL109fz+dvd3zf06iFz2awr+PSn4Vtf28aqypU575nFpRzPO0ZTeAHV9T09QSryAueg1KK3cRNgzc/Pp7S0lKamJurq6li5cmWfafbvdwftSAdHR2P27NkAHDp0yM2L1k90v7/1zZ49my1btlBXV9fvcpPJJEeOHBmzcgohxHik5xXgAKaTJKl7sLqDOmqQB6V2PE7h5OElmp8ojKTFrS/+md8vfDOmA8saYFt6V2jA9u2Z1pYaGuFBkumLodub1TFk1XlYf8GcJRhNB2j17mBStAqAy+p2kp9sorxLAQ54TPRw/pisTzNNvL2ebMQlwDoiKpVyc7Bm0hwrGeSqHzPK51CyN4ZR7PYMM1M2HUMOsKaIZqUISBXmk2hsZVHjPvf9zi50f2DEuW41r4/8rJwDS0/ZRHQF3XHyvGABYb8McCWEEBOFG2DtzPytEglauoaetlGhiBo+yOr9c1YDrNkPDbPGT+gzWTrg2k+uV6OomJKuA5womIXCi62Dx1EsniGxqPNhXD2yXb58OQDPP/98v+/v2LEDgFmzZo16XQsWLMDn89HR0cHOnTv7vN/V1ZUJoqaDsdll3LZtWyYVQLZdu3ZhWRYej4epU6eOupxCCDEeaYZBh2ERNxPETLfFqq3sflutKuBAMTyxfgqld33vnJd1PDCSNnoyhrL+SCjZheH0Sp8QiYBSmZ+QDLoyIRwp1PlzoB1fvI7123/OJc/eRXHbHh6bo2gOQHPAHfhN849RKwM9N0UAQCKZGmDiXmpre37GUO29tZmfC0oqSSKrdb6mICCj8fZx19t/QlEilvnbozQi8aEF9Xu3YL1uwY18sGEKXR1tRNpaaIt1jKpsmq6T7xs4gKoBhRJgFUKICcPobsGapmyL1s7YkFM3Kg2iHT0xHu0c1sk33bIp8zMcemExXpXiQy88xfzGI0xtO80Vh/5M2eTKs1RSMZhxFWDdsGEDAC+++CIHDx7Mee/EiRP8+c9/BmDFihWjXpfH4+HSSy8F4Gc/+xl2rzxtv/3tb0mlUoTDYebP78mdtmDBAiorK0kkEjz88MM58yilePTRRwFYtmwZHo9U2oQQE1eraWE4uU+F57do6A7sK87OpAdoGrang+lDTDI/odTWokUi0NRKSrVx3d4neePuJ1h86jEMbDS0nFyhefEoHmNcXZ7FSGkadd4o35zZQCTaiqlrWNEoN+6Fkpj7Y8ZTY5Yjq78WrInU+c1Dm632w0WZn/HOScSJZQ9ypRT+IeQVvdho/gChrOuAqZu0x4cY1E8liWe1YNV0g9DdP8SMxPFEkwSt0X8vfAE/unLLozsKWyfzg9LID0hdXQghJgotFMZwOnNeS8WidAzlwd+2bag9u/sMvhgc573K9LwCDpQZFMUirDm+mSsOPc+sPB+aMb7LPVGNq5riwoULWbBgAbZt87WvfY0tW7aQSqV47bXX+MpXvoJlWVRUVLBqVW5Hv2eeeYZ3v/vdvPvd76axsXHI67vxxhsxDIMDBw7wjW98g9OnTxONRtm4cSOPPPIIANdddx2m2fPUQtM03vKWtwBuEPbBBx8kEonQ1NTEd77zHXbv3p1ZthBCTGStHhtd9e12k31LHDVhbgvULL2Gx9/3n+gXY7L1bdvAtvnIVRbfmd5IsO4Yk/fs5bTexM2vn3Bv+jX3xh+g8l/+4bwWV4yt/3pM4zcPalTXQ00kjJNKkWhpRu+KY3RE8bdGx25lhtE3wDrUFqzA1vptbK3fNnblAeIvPU/ec3/GeOGlMV3u2ZaKx0npWfU/BX5pwdqH7guQ9/a30hU06Qqa6GhEov0PgNibk0wRNbNuZA19zG9kDX8AzerAdmycXpcfrbaWQgmwCiHEhKHpOl1GFMeKYjkW209v7x7o6gxpAmprqb2pnfdfGaMxPaiVpqEVFhEe5+mBNE1jU0lXTitdnzOuwnwXlXF3tHzoQx/ic5/7HE1NTXzta1/Lec/j8fAv//IvOQFPAMdxSKXcG4ihNv8GmDp1Kv/4j//ID37wA7Zs2cKWLVty3p8zZw433XRTn/muuuoq9u3bx1NPPcWjjz6aabWadt1117F06dIhl0MIIS5EbYbFDKv/G+mYh0ykVSmorggQGOdPgM8qwwDNBtNg9segOhImlufln3YfBKb3TKdpTCoMnbdiinPDTiY5jFvZL1m+YcyWq2kaPiM3ijTUFqyr5z+NM19hoBG6t3bYXdT6o5IJajsLKLQ9OGjs07wc9Hd2jxY/vsVjiZ7UHd0kRUBfmj9AWM9NS9EeiQ0wda5kKoml9+xTTTcIeg2u+VBB5rVWGNWxqAUCmHaUpFEIwM5y93VHgytNjwRYhRBigvnm237Ax3/xv3T5yrCSbahEnOZokpklg9Svt23DnA4f3JHHs0v17kYPCmND7bhvwQrQ4LOZvL8Nr9ePN2FhVo7/Mk9U5zTA+tBDD51xmrKyMr72ta9x33338dxzz2F1j/Q5d+5c3vve9zJ37tw+82zYsCGTXmC4rrzySioqKvjJT37CoUOHADBNkw0bNvB3f/d3eL19R4zVNI0PfvCDzJs3j1/+8pe0trYCkJeXx8033yytV4UQF4V6b5Ka5JkTxwesGDXL559xugktHGbTRqi9ozrz0v5T26hoa+C6/Xs4XrAQgIBKMW/OlPNTRnFW3PzPbrDoTz+GmspqaoBnDj7F2WrL7evVJWyoAdYKy8uqeAFBx6D9SAplW2jG6KqJifmzKFznYWHLPPaWLQC/l7DeRkP8iWEtx6ndQNywcFB47/ox3lnzRlWuoYjFc89tuqPwSYqAPjSPhzwz92jujMYHHEA2TSlFNNHr2DQMgh6D1k+2jl35/AFMp6eVeLpEBhp4PBRIgFUIISYUo7CYlN5OzCwDwIl00tg5eM+KlN/k+mgFsYKynNd1n++Cebh6oMABoqxSBbmDZ4lzaty1YAXIz8/ngx/8IO95z3s4deoU+fn5lJWVnXnGEVqyZAlf//rXaWhooLOzk8mTJxMInHnAiWuuuYarrrqK48eP4zgO06ZNw5BcF0KIi0SbYaNUvN/36uxfccuJaaQML5NO7iY07e5zXLpxpLo682t2S6zae2vRWl9kQf1rVHQ2EvHlUXG6jkDx989DIcXZUl1ZDUDN7p7P3pmlozvdLSPHuBLs9eQGAW3bxrIdzEHy+iqlWB0rJOjoRD3FxE/ms3XzLpavXDrofINRjkMy7KMlNJWHJy3LvF6qCgn73kQkFicc8A+yhJ7lRE8epKmgmANF04g9+Wdmv8FgwcKZmPrZC3h2RuM5uZGDydiY5cqdaPKCuSMs28kU0ZRNyGvmDpqWfaw7NjE7t9eZ12OO+HgbiO4PEiBJl26gK4tLj3V/hgUFhAM+yXcthBATjFk5BZ/V86Buy+EXyJu3YMAHf8pxiBWH0LQke8oXEjXdh3EKKAn6L4hrf+snW+EPF9hgohPUuAywpgWDQebMmXPO1ldeXk55efmw5tF1nenTp595QiGEmGBWRgtIEWHdcfdv3VFgauiOxT7fSb4w+wQmGt5pOpsugMrJWTNAAG3TLZvgx7VEjjxLQUcn+Y6DHR9a7kIxNDH/+X3oOVDX5nSrVnC7QI+l3gMxKcchcYYAq9PeSsgxiJl5HC65DEczeG7zXl7aX89lk/3MWFFNfkH+sMphtzRh2RYtoUXEPLCsAQ4VdpdRC/LySzu4qnbNGZeTvGYD+4qm8OUrVrp3PAdeRDvwEqnSEF+pupZp5fnkLa0Z88Ec2mMpN7VHd5C14J8lN/JAggE/KAe07mPMsogmuwOsA1CpFFGVe0yG/H17jY1W7xas2QrzzhzgF0IIcWExyirwmJ0sbXD/NvUI0Y4OOuJWv70WnNZmlG1xuLCC0mQZugKU22t5akn43BZ+NKTV6rgwrgOsQgghxq9N26v5mwUH+7w+ub2dmnqHO34Pjg6romG4iBuwDmrTJpw5HlJRNwBg6nJZvhikW7WeDd5eeepRDomUQ2iQ2JXd2ozpKKZ1LuJAmRuo3Lz/aUzdZPNWqDnUxmUb1lA1u3LI5XDaWjgVLsfWvdgahJIQ8YLd/azlf5/fyGWXrcI7SLd75Th0NpzizwuvBCCchJgJoPC3xHny3l9jKJtL999K9Z+fH3LZzkQlE7Se7r4z6w7c5p+F4N9EoQcCmCqBpbm9v5SdIprM7f5fW7UN7nVb12y6ZRMqlSTeK8AaDJyFAGsggMfu6ve9oqKCfl8XQghx4dIMk1YzwtRkHB0/urJQGx/h9Mq5/QZY7c52cBQR/1RKs7IDea0Eq+ZPPYclFxOB3MkJIYQYsYTWc+Nq61A3q4CVh3ahurO6RLywNR9qzlP5LgSX1Q19lHchzsQwDTyaRUp1RzIdh4Q1eB5Wp7OdhKeAE343/2/M4wYzLeXmwX/1tcfBMCkvqWVSwZlTKLnLbONw0RSq6uG56d0D32XxWjq7T7VRNa14wGUk977GtvK5tAQ8GN29ydMt5k3HYnt3vHdrWTVfb2impLxkSGUbtNyxKF1vuYa2qSvA6unCXhDyDTLXxU33+TGcBJbeHWC1LGJZuX+LLn2OpSctePop9yHSLUAqRbTXKMdB39jnQ9X9QUwniu1YBFJgawrDcRtDlxfljfn6hBBCnH9NXotDhS1M6poMCvI1xan2OO//Sk/P4013uH2InEgHUY+fLt80FLCvuypRHNtPfsEF1IJVjAuSeEgIIcSIxQyLeY25rVhnNB0F4Nbr4Zr3Gvzbp6rPQ8mEgLA3nPm5aJgmPq0nMKiUImE7g8wATmcH1S0L0IDq01Bd774e8wAaKBSvbv89W18/NORiJNraOFowGdxF4HQPWpp+oSsZYf+xxgHnV7bNiY9/gtfLZgFumeY3u++9NNXNjZZm6TrPbD2AUqrvgoYpseNV/n1WlIfLFZZO90jCUFQ8vBQJFxPNH8BQWelNsgKs6rnnWNsW4LpoOetjxYS6nyepVJLYpqfdFAy2jaYUwUFSCoy4bKE8bvlzjECyjZgHoh73wZ+uYErh0B4WCCGEuLA88s9PY9OIo7m96VQkwvGGViKxdj7/aBuff7SNrYuKALA72nl5RjWhlE44CVX17s8nD0nPFTF80oJVCCHEiE3pgBUndqKh0xos5J+e3k1ZZ9P5LpYQ49pAuVnHgmYY+DSHSPoZuuOmCBiME+ngRF45YcfExg2ELmuAF3v1jDtS30o8ZeMfwoi6x5s6SZlui8RwCn6zUGde05+xA27eVduxaWzuoCOeIt+f23LRiUX55u03kB+YQ5dXzwRTU4koN+96gWByOQmzPPN6zG9woqGNuqYu5paNPJiuajcQO3WApjddxfLTuK1mFQQTUaZPnzzi5U50mj+Ak4pgGW6zH9WdgxUgURSipitIyIEQBnnxYpRtuQFWw9fTSlhxdgKsPh+aUhR27UFTa1Hd+cAXtZ0k7JPbICGEmIg802bhTzVlcr+Dxeq2Dmw9hKZ1ARqRZITae2tZcTiPkrIZ7tPg7prFlNZTlCdkXAQxfFKzEEIIMWIxzcZUNm84sgUN0P1+vJ4gph4hYtiEveGzGkwSQuTSDBNvVgtWlEPCGiDA2j3Ce3tbA5GFV4JtE/NAqDPJW1/+NXU3eqkvuhGl6Zi6gd0V4XBLlIUVg3etduIxDv7qMVDuIEJVp+DN77yVF5/4Bw5FkjiaD0M3cOJRDjV1UTW1sGfeaITPv38xpwprWeTxU1fstjjs9Ot0TtV4+vpJfOnLv2amVcjON9+EZbjBWRXrYseJ9lEFWC3N4XDhNFJm7vZdsvMlPB6pMg9E87s5WDX37hRlpYilbJRSJPODJAyN4wVVtIRmUXOqk+Y33Uh+KkqsYBb43H2tWRbBIQTuh102TcPWdcLJk6w//CyNoTLyEl1coknKByGEmKg00+R0XoSwlWBmm3u+d379K1aqRRSUHQcFiUQM45nn0MzrCaQUVfUalga+VJxLX3kCfdmK87wV4kIktUUhhBAj9qPH/NRNhqKY+7cP0BzFpoe7gxytYz1GuhBiUIaRG2B1HJIDpAhQKCxNcbRoUua1sGNS8JH/x4zLfsB/HTvMJ3/4VU44QXf6aBf7T3fkBli7g7RAZgTbrq1/5niwDDojAGi6zrzp5fzBD8GWk0R8s7qXF+Vgc0+AVSlFxzNP0JR/CTPa8zAct5sewIwPfZAb1s3H1HW4xQ3Eej5xFW2BNeDYKNumoaWdpvYiSvL8aPrws2BZuqKuZDrhpNuQxdag8sRRFk+rGPayLia6P4DhxHtesCyiSQusFMrU6fTPwjZnA9Dpy+fvlyQItb/A+siinnmUIugd+wArgJVMAiYLG5tY2Oj2sDAnzTsr6xJCCDE+NPgsKrsagGmAm4N7eftitk2u5OmZ5ejKZhaKJiPMlE5AKTQNql9/hYLOjvNadnHhkgCrEEKIEdMWLYb2AzQH3ICOrieZMQZ5EIUYCzW7L74Av2b0ysHqqH4HuXLicbqO7CbqM9g3/2rI+t5OryhE0zSM8kqsjtexg24rjm2ntuBpmE9rtIyioJub7E3zXqYmGkbDw//bu5c8zWHXN79L0lNBuhi65TCrLI/v/s2DPPdXb+bZWTMBC2d6lIaOGO3tEcJYtBw7xhd/9zAxjztKXjr/aUVHB29cPc8Nrqa3MxBiVp6HFjuGrQd4/tjzXLp9O796IM5l161h8mUbCFeUD7qvrv7RBnc9hsaf/u5PtDU1cWphVSaoa1qKtacOo+3fMdTdf1HSfP6cHKzKdlMEONEoeL3EvdPxZMX48ygloMqwNU93m1eguOSsBViXbz/NNz9QhoN7zBq2g6m0M8wlhBDiQvaHj2zl85+4EVvvDrB2toMe5nhBGFuHpadhZ7mbm1vRU+eYXn/E/WWT9MATwycBViGEECOmo1HZpVEf6gnOGKnBRywXQpxFhomXXikC+snBmty9Dcs0uHP1VLzkE7BwR1dPpZhf4Q7opPsDpDjJyhNLSRkeTD2C09bK1uNtbJiRj93UwFymMiu6hFP5U7jn63cSXn0p26dWMKMNwkl3XdMb6wl6TVRpOYWxBg4VJrB1H/Gjz7Le4+Hh3Zvx4dBsm3TpBTjdsa+YCVNaunjLwz/qk/dV0zSaQ3Dzi4f5/YKFgMXmQBsAzzz/DGvavNRcvpZL5lWiaX2DaYmrLmfVzIMkDT+H8yr556/8NVdOm9UzgWkS+MD7mXHZf4/0k7hoaL1bsCpFVzSOE9VI6iZJs4iohjtgmgZepWEXXA/RrGVs2EDgLKQIAPdYuXfvUtpaNqMMHaMjijZFAqxCCDGRGWUVOKnjHCxL4ege5jcpghYELHegQ92BLi84mpuKyFQaebF2GkNdNIVMas73BogLkgRYhRBCjJiGhsfuGdHbTiYwFywB5OZViPNBM028WlZA1XGI95OD9Vu//Cirpi5jV+U8ljb0tN4o6ehkcoE/M91pX5KQv46i5EL2FliYLz1M2+/gsG7gdSzqS6+hNOl+3/cWWCx46RmUOzAvES8o22bRT+9yy+bx8uj1s7DbTxHxzgTHYttrjzOzVRFOujc7VML8Znf+cm8BN/zsmwMORvTlf36YFx6rBbWQ+c2wr8RdJ8CmA09CKI/SwjDzy3NzqjqJOPGGY8TmBDlZeCWabtLcqbG3pIS87qCwGQ4zZ0ppTqtZ0T/N58dwYjmvpZIp4lFoM/yEk9DpBUfTCKaUO3gY7sjORndLUsMw8Jlnb19rm56iboqO7ijIgxXSMkkIISY0TdNo9CYpih2hKDGXumKY2+K+Z3ffpoSTMLsFghaAYtbJXXIHI0ZFAqxCCCFGJdDcRdJJABrJRAytRKomQpw3hpGTIoB+UgTYne0knEm8Om0S4Aa8Yh7QFCzypXJafJ722dy2/QBPz55PzKMDFgfyoU6zQSnCSY26YpjTAvNb6B4oArZXujcwhxZo3FZRkFne//e+n/D8376N/105E3DzrmaKqsPy7u75Qcvi+u98ltLwwIMRmaUVFDXXkxc/haNPouq02xrFpbBntLB512HmlS3N2SantRkSSdqCS7nkuEmXF3SlcLSeh0W60lhcmT/EnX5x0wwDmxjL67v3Xtt2mD2fro4Ep8OlVJ3W2FahaPcroh432Apkt7Mm5DP6bWk8loz8gjNPJIQQYsJoNlKE7NcJpCYBIQKW2zumP9NbjjH5+OsQHvlgmUJIgFUIIcTIdLcA0mpraT/4FABKripCnFd9crAqh0RWC1alFLEjBwkfLcZUbsvRuu4WpwWxUyy7/as5y3v2MwfY87t5hBMnCVhTmd/sTm9pipgH1h2Hl6a60wZT7v+ODsvqFetu/wzrFkzOCZyZU2dS2nqSUKKRua1lGE5PUNS96VGUNR3lHQ89RGE4cMbtXbLlJKXvnc2CtoU0hCuImYXsK+nuar7/adYUlVDX1MXcsp4bJrutFVuDpDk5Z1k7u1O2aprGv77zbwcN7opcMcPCZyVImj6IRFCWRSQS51S4zB04JGvaQPdxEvNAnu1+VgWhM3/Wo3Ux5mQWQoiL2S9/0MpXb7uCque3sqd0HWBiWEmKWp7kzbs12j15XL2tnunNCYLtHRwslkYiYnTkVlgIIcTYkq6XQpw/hoGvV4qAdIDVuvIKvlt2iLrSy6k0CjEtt7tcKAnT205w5YGX8X13Zs7i9GCYZEcHu0peY8npElJmgGUNsK3Cfb/LC8saYPapvcxvaeb+mko6PTbPVR7lIwu/16d4mq7zsQ8tZuVTLzC1bTXHCycTSjpM9Zp8/9JT2J4mlAH/OITgKoDu93Pbqfm8fnIzizUNR9f53lU3EPe4rRXt1maefOw5PKvmMGOhO3K8E2nnVF45AcuD4UBBHNr8sLQBNKW4NNHJpdVzhrnjL25TS2dzUI8T9/gAi7W2RXtXnIZwCQBV9fD0zIHnLyoInpNyCiGEuHhohsknP/U7tlwyA2/baa7dEqYzehxfKk5pCzyxSqO2QxFocceV+Le/KICaagDkbkaMhARYhRBCCCEmCM3MbcGKckhaDlY8RtRI0RCazaTOwpx5QrEOrtjxJ8IpE83jpbfqA13c8GkfP3v8Dzy6YinH8sowVJgb95wiv6uVS/ccoPT4CTRN4xP31gIwmcoBy/iHf3gKdf8G/mHmJgIpOF3k4Rffa+AvRrjN+qanWNb9uxPp5F9++Uu+vG87/rjF5v1PY+omTuQN/IXPS/msGTidHRwqmeFOrwNKoRTMaDnEpMb9bAhMQTfOzoBLE1XC1DCdOFAACn777D28FG9llVbRM5FSXHLkJbZXXIKtuS1ZbSwMpbFgUtF5K7sQQoiJy8grQGvt4L1PdDCtqZ78N2zIem8baO3snGIQ9obZdIf0dBCjIwFWIYQQo7NpE9Ys6VIjxLig923BqlBEjxxmf0cDB0uuZlmDm281ZgKOxdzXfsth1cGKRjXgYk98NQFfhTe0teB0tGNOmoJ27RsBA06czEy36ZahtfnQNj3FPSPcxMHo4TxmTy3jhkcP8eTsacxtBbBobXyWP5UW85czpxNvb+doyTQ0DWKmwlaKt3V0sCwSg8BUaYU/Ap+79it86n+/kfn70tNeoDu42p0iwmc1Udx1ilDiOB3+qUS84LUd3njkNUrLJMAqhBDi7Fg1/wr3l/nkXuPvrSX82DYAaiqrz3WxxAQkAVYhhBCjVnto4MCMEOLc6d2CNT2IVFfDabZNqcHR3Krf3hJwNMUNLz9PgVeBt3BIyzcKizEKi90/xmkg0r+0hmv2v8orkw0OFE/O5HZ9Zdtv+H77t7n8gIdSfTpVje70vlvex9wrFoNXWq2OlObz47Fjmb/3u5kBmN8MYcfdrzv9daQScco7X8FrnWBpvZ9JnaeZ3WmheSXfrRBCiLNkgPrKpls2wY9rz3FhxEQmAVYhhBBCiInCMDA0MDWFpTS3BatS1P33DzkdrqC6vnv0dk3jmNrJD/6plE237DzfpR5TZuUUfM0dzGp5hV2V1xLxuPlc86M2015RNBteSrOmn15RSECCq6Oi+QN0+LqIeCGcBKXcQaxsHSKaTdg2qNePEelspSTmpyR2gkWNgAIjYeYMhCaEEEKcM+P0YbG4MEmAVQghhBBigtAMt2rnywRYbVLf+zZb9GLCyfREGle97f/xV+vm4jX181fYs6j4dCcfTsR54YGH+O/9WwCIKYtY/kwAFrS702lKMV/yf46aHgwTTDXSgjtw2qFCsDU3mG9rCp+dZPnMhRT6E1y5K8GmRb7uSD94p848fwUXQgghhBgjE7NWLYQQQghxMcoKsAKozZvZWuEQ8ULEC2gaWkEBq+dWTtjgapru87NgcjGonpy0tgbrjoOyLLAs/Mk4M0pkBPvR0gsKCeoOIacDvVfGGAUEYqd48n1PUbO7lU/duoxd/gg/XZziP5ZH8Dzx9PkoshBCCCHEmJrYNWshhBBCiIuIZqYDrE7O65bu/rT5FEXJOPPL885H8c65cEkRodRplp6yCSeh+jQYDqQ7pC89eQBTl+rwaOnhfBwN8hOHCaaguh7MrEDrzIb9md9tQ2Onr5M/hprY6euU9ABCCCGEmBAkRYAQQgghxERhuLlEfb96FAqngGVBZc/bynFY23IUQ784glpGYQnByE50+3LA5EAxVNW77/mtJIvu+vZ5Ld9EoRkGMcPBjNZR1jmJllAZtgYeJ8WS+p0UNR/LTLvplk28+hk3qG3okvtWCCGEEBODBFiFEEIIISaIdA7W5ybHOFJo8YajPQFFgNmmydxf/fw8le7c0wuL8TsdXH7oaUqj6/nzVHe0ep+V4Ko9z5I/5zvnuYQTh640NBRX1T1LQ3gyO0uDbNh3BCPagdWrlfCq+Vecp1IKIYQQQpwdEmAVQgghhJgouluwKpWgVypMSgIBrrnraxdVl2yjqATd9FDW1c6bdz3B8fBU1hx1mHlgF/ntHeg+//ku4oTR4rEpSbjpF2a0nWTZafAfOQWFhdRUVp/v4gkhhBBCnFWSdEoIIYQQYoLQvF60H9+LL9XOuuPuawoIODZv/p8v4zMvri7ZmmHwel6CohgErQQffaGO/BNbaCyCupn557t4E8qO/DgAp8MQN+Afn4lS02RKcFUIIYQQFwVpwSqEEEIIMUFohom1axd/1ZLHa5NOcyq/gsKOVm740X+TH/Ce7+KdF7FF8zm08RXmxLx4YkmWL1sHmzad72JNOIWzFrOjfRvKCWDEU/jaYxAO97+vZf8LIYQQYoLRlFK9e5AJIYQQQogLVGfIg+MxUbaFrXswbJvCSOJ8F+u8enVKz6BKNcdS57k0E1PtvbU8d/Q5Hv+RjaEbXDZ9vQRShRBCCHHRkBasQgghhBATSF6XG0Csvbc289rFHuZadcI530WY8Dbdsonae2sp8G8730URQgghhDjnJMAqhBBCCDEBbbrlYg+rCiGEEEIIcW5IigAhhBBCCCGEEEIIIYQYIf18F0AIIYQQQgghhBBCCCEuVBJgFUIIIYQQQgghhBBCiBGSAKsQQgghhBBCCCGEEEKMkARYhRBCCCGEEEIIIYQQYoQkwCqEEEIIIYQQQgghhBAjJAFWIYQQQgghhBBCCCGEGCEJsAohhBBCCCGEEEIIIcQISYBVCCGEEEIIIYQQQgghRkgCrEIIIYQQQgghhBBCCDFCEmAVQgghhBBCCCGEEEKIEZIAqxBCCCGEEEIIIYQQQoyQBFiFEEIIIYQQQgghhBBihCTAKoQQQgghhBBCCCGEECMkAVYhhBBCCCGEEEIIIYQYIQmwCiGEEEIIIYQQQgghxAhJgFUIIYQQQgghhBBCCCFGSAKsQgghhBBCCCGEEEIIMUISYBVCCCGEEEIIIYQQQogRkgCrEEIIIYQQQgghhBBCjJAEWIUQQgghhBBCCCGEEGKEzPNdACHSLMuivb39fBdDCCGEEEIIIYQQ4qJXUFCAaUrocChkL4lx4/Dhw3zmM58538UQQgghhBBCCCGEuOh95StfYe7cuee7GBcESREghBBCCCGEEEIIIYQQIyQBViGEEEIIIYQQQgghhBghCbAKIYQQQgghhBBCCCHECEmAVQghhBBCCCGEEEIIIUZIAqxCCCGEEEIIIYQQQggxQhJgFUIIIYQQQgghhBBCiBGSAKsQQgghhBBCCCGEEEKMkKaUUue7EEIIIYQQQgghhBBCCHEhkhasQgghhBAT1K5du3jrW9/KlClTCAaDzJ8/n0984hO0t7ePyfIbGxspKytj0qRJtLW1jckyhRBCCCGEuNCY57sAQgghhBBi7B08eJD169fj8Xi46aab8Pv9vPTSS3zjG9/gj3/8Iy+//DI+n29U6/iXf/kXmpqaeOihhygsLBybggshhBBCCHGBkQCrEEIIIcQEdPvttxOLxdi6dSszZszIvP6BD3yA73//+zz44IO85z3vGfHyH374YX7xi19w44038o53vGMMSiyEEEIIIcSFSVIECCGEEEJMQK+88gpLly7NCa4C3HDDDQDs27dvxMtubm7mgx/8IKFQiO9973ujKqcQQgghhBAXOmnBKoQQQggxAYVCIQ4dOkQ8Hsfv92de37lzJwAzZ84c8bL/9V//lYaGBr71rW8xffr00RZVCCGEEEKIC5q0YBVCCCGEmIDe9a530dzczHve8x6OHDlCZ2cnDz/8MF/72tcoKyvjbW9724iW+5vf/Iaf/exnFBQUEIlE+MhHPsJ//ud/cvLkyTHeAiGEEEIIIS4MmlJKne9CCCGEEEKIsaWU4pOf/CTf+MY3cl5fsWIF999/PwsXLhz2MhOJBIsWLeLQoUN93svPz+eee+7h7W9/+4jLLIQQQgghxIVIWrAKIYQQQkxAr7/+Oj//+c8BCAQClJaWomkau3fv5u6778ZxnGEv8+677+bQoUNomsbnP/959uzZw+7du/nSl75EIpHg3e9+N6+//vpYb4oQQgghhBDjmgRYhRBCCCEmGKUU73jHOzh58iR33303HR0dNDY28vrrr1NTU8M3v/lNvvSlLw17uQ8//DAAH/7wh7nttttYsGABCxcu5LOf/Sxf/vKXSSaT3HPPPWO9OUIIIYQQQoxrEmAVYoj6y6bR+7X0373/H+i1/t4bKGvHQOsayvoHK8+Zln2m9Q62jUN9b7BtOtP7Q93Xw9mO/l4byvRDWf5wjqPByjGUfTvUz3so6xzOMTKUberPmco7UJmGeiwNdT8Pd9uGs+ze6znTNgy234Z7DA9WjpHMP5TXxnp9Q91PozkXjXY/DraOwbZ9pOsbqLwvvPACe/bs4QMf+ADvfe97MU13XNOFCxfy29/+lkAgwL333jukZWU7fvw4AO9973v7vH/ttdcCsGfPnmGV9UyvDfX8czaO6eGcX4fzeY/mvD/Sc9Vw99tgyx/puWA015/Rfp5D+eyGOv1Iyn+mZfdnpOepM23jYNMPZflnmmY0+2ygZQ13+4ZbzuGWazjlHItjoL+/x+qcN9z19vf62TwXj3bZZ1rfmT7Ts2U4x+ZQyzPc+YZbhpGsf6wNZxuHsj/Hcp+dad7hLHMsnGkbR7OdIynHWCxrvJMAqxD9yD4ZNTc3A6BpWqY7ZUtLS8607e3tADQ1NWX+V0qhaVq/r6VPaI2NjZn3mpub0TQtZ/1Azvqz5+mvPK2trTiOQ0dHB0qpQcujaRrNzc2Z1x3HySwne7p0ebLfcxyH9vb2zLzpMrW2tmbWm36voaFhwPfS29l7G3t/FtnbnH4/XZ4z7ev0+rO3MXub059d9rKzX2trayMSiWQ+m7a2tpzpI5FIzmeWPW8kEunz2WR/br2Prd7Lzt7nra2tmeX0/ozSn3d6H6f/z54v/Xn3PnbSr6W3ofe2t7e3o2lan+3pfSyll5e9DZFIJOc4Se+j9D5Ly56nv+Wnty+9DS0tLWiaRnt7e2af9ncsDWUbs/dzR0dHZnm9j5OBlp0ue9pAn3/v7Wlra0PTNDo6OnKmTy+z937vvY502YB+yzrQNvZ3LGUfL0M9Nvvbr9mv9f7MsteTvZ/T82UvH+jzne99/klvd3o/9j4vpNcx2Pcl+/hNr6P3sdr7fJv9d+9zW/Zr6XNQepnpv7PPTf11z8/ezt7nvnR5s7/nvc8p2dMfPXoUgMWLF2fmT6+7oKCAKVOmZIKlwzk/5efn5/yfXbb0+tPXuOGe99Pzp5cx2HGQfW7vfe7vbz/33p7s70jaQPs7+7o62DGVfb7vfaz3t47ex9RA25jennRZBrvG9V5H72N7KPst+5jufZ7vff7pb5uy93fv69NA5ymgz3mqsbGxz7E7lG3sfTwNtt+G8v0dzn7O3rfpa1V6PellZ6+j97mg9zlxsLpXWvaxffr06T7HT/Y2DnReGayOOdh2DrSvR3Js9q7TDnYez76Wpa+jXV1dOfux9/UYeo659HGZnudM+7n3/UDv73v2OSK7DtFffSz7/97n3exjYCjX8fS2Z6+7v+Oi9zYNFuzofT3ur57cu347nG0c7Fw80LU8u37Y3/7ur+wD1Qt713Oh59hJ76Psuln6O5Xe7mzpvwd6PVs8Hs/s5+Ecm/1tV/byB/rcsl/rr1zp9WWvJ/1af9fH3tuU3p7+til7+vT/6emzlzPQe9nTxOPxIW1j733T32c50L7OXt5g9y2DfW7p4yn7nvFMx8FA2zTYPuu9nN7zZG9H9v+9j6eB7jPSn+Vw9kv29vR+vb9l9fc9Hcp36EJhnu8CCDEepL/4+/fvJx6P4/V6OXToEEopHnjgAVatWsWb3vQm5s2bx759+/jc5z7HypUr+djHPsa+ffv49re/zaxZs/jzn//MokWL2LNnD0uWLGH16tU88MADVFdX8/LLL7N+/Xpqa2tRShEKhfjMZz7DjBkzOHz4MLquc/3117Ns2TI6OjqYMmUKdXV13HfffdTU1HDJJZeQSCS48847WbJkCTfddBNVVVXs3buXL37xiyxYsIA9e/awatUqDh48yLRp03j11VdZvHgxu3fvzpTn/vvv58orr2TmzJncfffdrF27lje96U0kk0m+9rWvUVNTw8KFC/npT3/KunXruPXWW9m3bx9f+MIXWLlyJddccw2tra389Kc/ZdWqVZSUlPDQQw+xZMkS9u/fz+rVq3n++edZuHAhixYt4pFHHmHZsmXs3r2bWbNmceDAARYuXEhtbW1mG++//36qqqpYtWoVkyZNorGxkcLCQhzHobm5mTvvvJNFixaxYcMGamtr2bdvH1/84hepqqrilVde6bOv05/DwoULM+u/+eabaWho4J577mHFihVcffXVhEIhvv3tb7N06VLWrVvH8uXL2bNnD9/73veoqalhxowZPPTQQ3g8HjZs2EBxcTGPPPIIq1at4g1veAOapvHDH/6Q1atX8/a3v519+/bx3e9+lzVr1jBlyhR+97vfceLECZYsWcLu3btZs2ZNZl8fOXKEBx54gJUrV7Js2TJ0Xefhhx9mxYoVrFmzhkAgwO23387q1atZunQpGzdupLq6mqeeeorq6moWLVqUmf/AgQPMmTOHV199lVWrVrF161ZWr17NkiVL2LhxIzU1NUyaNIn777+fNWvWcP311wNw4MABfvKTn2SO5b179/Ld7343s+0///nPsSwrc7w8+uijnDp1ilWrVrFw4UIeeOCBzPI0TePIkSNs3LiRtWvXMm3aNB599FHa29u57LLLuOyyywgEAhw8eJAnnniCa6+9lilTpnDw4EE2btzImjVrmDlzJj/4wQ+oqalh0aJFPPjgg6xYsYK6ujqWL19OZWUlDz74IEopli9fzpEjR1i5ciXr1q0jEAhw4MCBM27jvn37+M53vsPy5ctZs2YN4XCY22+/nUWLFnH06FHe+MY3UlBQwD333JPz3Thy5AgPPvgg1dXVXHPNNeTn51NXV8fPf/5z1q5dy/ve977M/ktvy69+9StOnDjBqlWrMttTU1PD3r17sSyLdevWsWPHDtasWcOsWbPYuHEj8+bNY/PmzTn7/bHHHuOqq65iw4YNPPXUUzz44INceumlbNiwgf3793PPPfewcuVKrr766sw54Tvf+Q4rVqxg1apVhEKhnGPpkUceYcWKFWzatIlVq1axefPmzDmuv2PTMAwefvhhli9fnrOvf/KTn7B27VpuvfVWdu7cyfHjxzPbeOjQIZYtW8bUqVMzy9q8eTOLFy/myJEjrF27lssuuwyPx0NdXR2PPvpo5rhpaWnhZz/7GUuWLOHqq6+moqKC22+/nZqaGq666iqqq6t5+umnufPOOzFNk8suu4z3v//9mXNh+rNct24dTz75ZOZ4Sp/Ld+3axfTp09m5c2fmXKSU4u6776a2tpa1a9eyd+9edF3nZz/7Web8EQgE+MxnPtPn3LZv3z6qqqqYNm0aDz/8MEuWLOH1119n9uzZnDhxgtWrV/Pcc88xZ84c6urqWLJkCTfffDPLly9n8+bNdHR0ZM5rX/jCF6ipqWHt2rVUVlbymc98JnN+Sx9/W7duZeHChezatStzjEYiEfbt25dZ/zXXXMOpU6cA+PnPf04oFOL3v/8969at47rrriMQCHD48GE8Hg9f+tKXcpaV/t7cf//9OcfAL3/5S6qqqpg8eTKbN2/m/e9/P+vWrWPNmjWZfXv//fcDcPToUb7whS+wf/9+5s2bx+uvv86iRYsGPO9PnjyZHTt2ZK4ln/vc53jyySf5wQ9+wJo1a/iXf/kXTpw4wec//3muueYali5dyte//vV+r7O993P2uSH9PQkEAnz7299myZIlLFiwgNraWvbu3cu///u/s3z5clasWMGDDz7I3Llz2bt3L2vWrOHAgQOsXLky55hKn6P27NnD3Llz2bFjB9XV1Wzbto2FCxdy7NgxqqurWbhwYc42Ll++nJUrVxKPx3nwwQdzjvXPf/7z/R5Pc+fO5ciRI6xYsSLnGvfoo4+yePFibr75Zmzb7nMcJBIJ7r77bt74xjeydu1abrvtNhYvXpzzuT3//PPMnj2bgwcPsnTpUvbv35+pC2Sf99rb27n//vv73ab098Tn83HgwAHuu+++zH6bPXs2r7zySp/z1H//939TW1vLW9/6Vurq6vjSl77E6tWrmTt3Lhs3bqStra3P55nexkceeaTP97empoZVq1axYcOGzOc5Z84cjh49ytq1a3PqJun9duDAgX7395w5czh+/DirVq1iypQpOdvo9XrZuXMnP//5z5k7dy5f+cpX2LdvH1/60pcy1+ef/vSnAFxyySUsXbqUhx9+mJqaGv74xz/22aZly5axY8cOwH1wcfDgQX72s58xb948tm/fnlP3uu+++7j22mu5+eabqaury7nuLVu2jO9///sDnqNWrlxJUVERX/3qVzPfnZ/+9KcsWbKEd7zjHezYsYNf/OIXmePJ7/dntrP3MXrFFVewZs0aIpEIJSUlOfu6traWqVOn8qMf/eiMn9tdd93V57j40pe+xIIFCzh27NiA5/Hly5dnrmVbt25l/vz5mc+rqqoKXde54447WLt2LbNmzeKxxx5j2bJlPPvss8yePZs9e/awdu1aDh8+zNKlS/nNb36Tc5zfd999vPGNb2Tx4sUcPnwYTdP46U9/ylVXXcXf//3fZz7v9Dli9erV1NXVceWVV5Kfn8+PfvSjfutjy5cv55lnnmHevHmZMlx//fXMnz+fHTt2oJQa0nU8vazt27czb948du3alakXtLa2cuLEicy1N/vYSV97586dy7Fjx3Ach1gshmEYfa7H69evx7Zt7rjjDlasWMH69euZN28e+/fv5wc/+EHm+jbQNi5YsGDQa0v6XHzHHXdk6mTV1dV9joEbb7yR+fPnZ+51du3axSWXXMK+fftYtmwZl1xySeZ788gjj/Spq6S/H9ddd13muFixYgUzZszg0UcfZcWKFRw6dIjp06fz6quvsnr1arZv387atWvx+Xz89re/ZcWKFaxYsYLHH3+cNWvWsGLFCjo7O7n//vuZOXMme/bs4bLLLmPNmjW0tbVx3333sXLlSlasWIFhGJw4cYLHHnuM1atXM2fOHL797W9njs0//vGPLF26lGeeeYa5c+eye/funGOzpKSERx99lFWrVnHppZfS2NjIxo0bWb16NVVVVXi9Xurq6jKvXXHFFViWxeHDh/vUUdPHdVFREd///vdZsGABR48e5YYbbmDKlCl84xvfYMGCBRw6dCizrPQ1/fHHH2fFihUsX74cy7L4v//7PxYtWsSsWbMwDIM77riDa6+9lhtvvDFzDZgxYwZ79uzhkksu4fDhw8yfP59XXnmFmpoaiouLefzxx6mqquLw4cMsW7aM559/PnMOXr16NY7jsG3bNtavXw/Q7zam9036vuzee+9lxowZbN68mZUrV7Jjxw7Wrl3LjBkzePzxxzP7uvf3pry8nD//+c/cf//9me9JIBDg0KFDPP744yxfvpynnnqKtWvXMnPmzMyynn322Zz7Jq/Xy1VXXUVpaWlmn9XU1GCaJidOnOD3v/89a9euZeHChTnft8svvxylFMeOHcus79ixY0yaNIktW7awbt06Vq1alTmenn322cw9y6FDh3jiiSdYvXo1K1eupKOjg29961ssXLiQQ4cOUV1dzeHDh1m+fDnFxcX8/Oc/x7ZtamtrM/tl2bJlVFdXU19fz3333ZfZL5MnT+aFF17I7Jf0eWDfvn3cd999LFu2jOXLlzNv3jyUUrz00kts3LiR66+/HtM0c5YVi8U4fvx45t70sssuw+/3c/jwYX7zm99wySWX8OY3v5kDBw7wwAMPsHbtWlasWEF5eTmGYZyLkNCY0FR/j7CEuMhEIhF+/etfs3HjxgGnKS8vZ/78+Tz33HOZ19I3UsNlmiaWZfV5Xdd1HMfJ/J+t9xNnn8/HggULMpXy0QiFQjlPtrLLU1NTw9atWzPl8Xg8pFKpUa+zv21MMwwD27bxer0kk8nM6x6Phzlz5mS6nw5H73L7/f6cp39FRUWsXr2axx9/vN8n+72Vl5dnWmfk5+ezbt26Ic0bDofP+FQuGAwSjUbPWIaRyi572kiPZYCCgoLME9KBlJaW0tLSkvnMJ0+ezMmTJ0e0vt56HycwtG0MBALEYrGcaXp/z/Lz8zNPvwdan8fj4dJLL+Xpp58e0rEzEl6vN3NTmDZp0qRMEA3c7Vm1ahXPPvts5rWBvtv9ycvLo7Ozc9Bp+ttnq1at4tVXXx3SOrIN5bjp/b31+XzMmDGDffv2ZV7TNC1zo3m29HeMjYZhGBQUFNDS0tLvZzuc82x/5xRd14lGo/z2t7/FNE0uvfRSysrKMut++umnOXXqFLNmzWLVqlWZ+YqKijItjwbS0tLCk08+yZQpU1i3bh267naGisVi/OlPfyIajXLdddeRl5fXZ97+zvv9vTZlyhROnDjR7zYOdP3sT3+fW+/vhMfjoby8PGd9Y22gFmPZRntt7W/+7H072DV3KHrv9/62aSjnG7/fz8qVK3n++eczy5k9ezZ1dXXDLgP03S5d15k0adJZ+zz7Owf2PhcPRzAYZOHChWzZsmXQa3/vY3kk6xzOd8fn85FIJHJe6+8YS9fXBtPf97D38eP3+1myZAmbN28eUvkGU1RURFtb24iux2faR5qmZYILA70/3PWWlJRQXFzM/v37CYfDrFmzhj/96U+Z94dSb0zrfe6Evvu/uLiYKVOmsHPnTqZOncrx48cpLi7O6Z0HZIKpaZWVlVx++eU89NBDw9q+gbah9/EeCoVYsGABW7ZsyZmutLQ0p1ddb73r89D/sXqm5QzHQJ9J+qF1+hhYuHAhBw8ezOz/UChENBodcV2xd520v2t2+mFZms/n49prr+U3v/kNQOYzzzbQcT979mwOHz6cc46dP39+zvFfWVnJ6dOnUUpRUVHBhg0bMoNrni29txH6P/aHasmSJRw9ejSnDlxQUJBp4Tpac+fO5ejRo5njoL9zfX/7urf58+dz+PDhzHKqqqrYuXNnzjzpBg0j0fs+YNmyZRQWFubcV/R3Hpg+fTpXXnklzz77bOZa3vuYmjlzJocPH85ZX3FxcaYFfPrvDRs28Oijj2Yav918883cfPPNeDyeTH1zvJMAq7jo7dixg//6r/8achBCCCGEuBC89tpr7N69G3BvODVNy1TMfT4f11xzDYFAgGQyyb59+/B4PCxYsOCMy92yZQt1dXXk5eVRXl6OZVmcPHmSVCrFokWLWLp06VndLiGEEEIIMfG97W1v421vexuGYVwQLVklwCouajt37uTLX/7yqFp1CCGEEOPV0aNHqfVKCJwAABVMSURBVKuro62tDdu28fl8VFRUsGTJEkKhEODm5vrd735HMBjkhhtuGPJyDx06RHt7O5ZlEQ6HmTdvHrNmzTqbmyOEEEIIIS4ib3jDG/jwhz+MZVmZQVvHKwmwiouSUorOzk4+9rGP9UkALoQQQgghhBBCCCHOv3e84x287W1vywzOlT1w1nhyYSQyEGKMaZo78r0EV4UQQgghhBBCCCHGp1/84hd861vfoqGhAdu2x20PZAmwiotWc3Pz+S6CEEIIIYQQQgghhBjEK6+8wsc+9jFee+21nMH7xlOnfEkRIC5KjuNw8uRJ/u3f/m1II6AKIYQQQgghhBBCiPPr2muvpaCggLe//e3jKl2AtGAVFxXbtrFtG6UUoVCI4uJiCgoKxtWXUgghhBBCCCGEEEL09cc//pEXX3xx3DWUG99DcAkxRvbt28cf/vAH9u3bh23bTJ8+nbe97W284x3vYNu2bezatYvOzs7zXUwhhBBCCCGEEEIIMYiqqip0XSeZTOL1es93cQBJESAuAs888wzf/e53z3cxhBBCCCGEEEIIIcQolZaW8p3vfAfTHD/tRiVFgJjQXnzxRQmuCiGEEEIIIYQQQkwQTU1N3H333ee7GDkkwComrLa2tnH3hRNCCCGEEEIIIYQQI7dw4UJuuOEGxlOn/PHTllaIMZZIJCSvqhBCCCGEEEIIIcQEMWnSJG699VZKSkrOd1FySAtWMWEdO3bsfBdBCCGEEEIIIYQQQoyBcDjMxz/+8XEXXAVpwSomsFWrVuH3+4nH4+e7KEIIIYQQQgghhBBimPx+P+FwmKVLl/IXf/EXVFRUnO8i9UsCrGJCchyHVCpFaWkpx48fx+fzkUgkznexhBBCCCGEEEIIIUQWTdNYvnw569ev58UXX+SKK67g+eef5x3veAfFxcXk5eXhOA66Pn474o/fkgkxCrqu4/P5uP322/ne977Ht771LfLz83OmqaysPE+lGx3TnPjPRcrLy8/6OqZNm3bW1zFafr9/wPe8Xu+YLWuiuFC/0/05VxWHiXQ+6X2OH61gMDjge+dqv02kzwcGP2+tW7eOGTNmjMl6DMMYk+WIC9fkyZPP+3FQXFx8Xtc/EoOd9y40Ho9nzJbl8/kGfE/TtAHfmzx58piVYbD1jJW8vLyzvo4zKS0tHbNlhcPhMVvWeFdTU3O+izAujedA3Pk02H4Z7j3mWFJK8eY3v5nVq1fzoQ99CKUUq1atYsaMGZnv83j/TMd36YQYBcuy8Pv9FBQUkJ+fz4c//OGcL2R9fX3m96lTpw64nIULF57Vcg7XYJW14Z4QB6p8jtWJdbCKWmFh4YDvLV68eFiV/IGCEIMFFU+dOnXG5RYUFAy5DGdDVVXVgBeRyZMnD6uyHY/HR9SV4nxeZAcy0LHh9XpZsGDBmKzjsssuG/C9wfbJWOUCGmw7BjomBrsBHOi84fP5Bv2enIsb1LH6zAoKCgYMaIRCoQHnG2gbo9EoixYt6ve9QCAw4HxjGdSZMmXKgO8Ntk3DNZKb0MHmGewYHajcx44d46//+q+HXY7+zJo1a0yWA4Nfq4ZrsHPHYDcMgx0HY7mtg52/AoHAmK1nuAarSwx0PLW1tfE3f/M3A8432PmyPyMJ/hcUFJz1/HAjuUYPVudds2bNgO8N90FtUVHRgO8Nd/+PxGDbMlwej2fAc/6yZcsGnG/+/PljFrSsrq4e8L2xeog+derUYQfZBztHDtZgYqDrdXNzM6tWrRpWGQZSWlo64HrO5wPMweoJg32Wg92TvuENbxizLtNj+XBooG0dyQODgZY1WAOLK664YsD3xvL8PNj3/Fw8HBmuwerea9euHbPPbcmSJQO+N9C587nnnkPXdX79619zxx138D//8z/ceuut43I/9kdTSqnzXQghxprjOKQP7aNHjxKJRJg+fToHDx7kq1/9ap/py8rKqKysZOfOnX3eu5DSCxQVFdHa2jrk6deuXcvLL7886vUahoFt26NezngTDAaJRqPDmkfXdRzH6fP6YPmAS0tLaWpqGlEZh2PBggXs3bt3WPMMtD1jqbCwkLa2tiFPP9j+mjdvHvv37x91mfLy8ujs7Bz2fPn5+XR0dAx5+pHkiR7JcTnYPh5sn41lHuvBPrfZs2dz8ODBUa9jzpw51NXVjXo5aSPdftM0///27i5Errv+A/D3zOzMdjObbLpNqmmsbpXEoDQ19WVNQCgWtEkvLAraRsQLCwVBbIjt4oV4JYhYFEzxDe9Sq14KgUbwpbkQgkkVak0lQqsmqSZ2s3nZ7mZ35pz/RbP5N+3uZnf25Tcvz3OVzDl75jMn520/OfM7Ua/Xly3HbJZz33znO98Z//rXv2ad1uy+0Iy+vr6YmJhYlfdaqPnWzVyyLIu5Lq+buaaYbzusVCoxPT29qOU1YzX+bWq1WoyPjy/b8pbz3Lpa5+m5VKvVmJqamnVaq16n9vf3x+XLl1f8fd7xjnfEqVOn3vJ6M9em863Lt7/97dfdnDFjtT7nfJZz35nrmNfMOWe+48Z8+9Rirwvnc+edd876+918x+nZ9Pb2xoYNG+L06dNvmbbY8+R82+bg4GCMjo7OOm2+dbbY3wFvueWWePXVV2ed9q53vSv++c9/LnhZ87njjjvipZdeesvr8+03c13nzrdd3HbbbXHmzJkFLyti8ddq8x2H57v+HxgYiAsXLiz4fZox1/FpPnPtA319fTE4ODjrtr5Y8+0b820DH/7wh+P48ePXjjlf+cpX5r35pZUoWOk4byxXDx06FE8//XRHln8AAAAAnWj//v0xPDycOsaCddbgXnS9N5erBw8eTJwIAAAAgIVqt3I1whisdBDlKgAAAEB7mG3M7kcffbTtytUIBSttLM/za1/9V64CAAAAtL4vfelL8ctf/jI+9alPXff6Y489Frt27UqUamkMEUDbmJqaivHx8Wg0GnHTTTdFf39/1Ov1mJycjKIoIs/zeOKJJ+Kvf/1r6qgAAAAAXe+BBx6IP//5z3HmzJmoVCrxhS98Ie69996o1+tx5513xsaNG6Ner8cjjzwSd999d+q4TfOQK9rC0aNH4yc/+cmqPckYAAAAgOY99NBDcd9990We51GpVCIiolqtRkREvV6/bt6enva+B1TBSst79tln48knn0wdAwAAAIAF2Lt3b+zZsydKpVLbl6cL0fmfkLb2wgsvKFcBAAAA2kC5XI7PfOYzXVWuRihYaVEzD606fPjwir1HX19fFEURk5OTK/YetJf+/v64fPly6hhtZePGjTE2NhbT09Opo7SFUqkUa9eujQsXLqSO0lbWrl1riJgmrF27Nqanp53nFqGvry8mJiZSx2g71Wo1siyLK1eupI7SVmxvizcwMOAc2gTXuItXrVZjamoqdYy2cuutt8b58+f9XrAImzZtirGxsY44F9xxxx3x6U9/Onp6emLDhg2xadOmripXIwwRQIt7/PHH4+WXX04dAwAAAIBZlMvl+O53vxubN29OHSWZUuoAMJ9u+t8OAAAAgHaT53k0Go3UMZJSsNIyZnbIRqMRk5OTMTExER/4wAdSxwIAAABgDkVRxPHjx1PHSMrtgbSMUun/+/5yuRwREdu3b4+TJ0/G9PR0bN++PV588cXo6emJm266KV599dU4ceJEqrgsQaVSMTbPAvT29hrPboGMk9Wcm2++Oc6fP586RtsplUqR53nqGG3HemO1rF+/PsbGxlLHaCvGg22O8+jiWWeshizLwmiYi7Mcv6NfuHAhGo1GXLly5S3fRu6G8ViNwUpLOH78eBw8eDDOnDnTtgfCbdu2xYsvvpg6Rlvo7e2NWq0Wo6OjqaO0tM9//vPx1FNPpY7RFjZs2BBTU1Nx8eLF1FHayt69e+PnP/956hhtx/G+OVu2bImTJ0+mjtF2br311jh79mzqGG1lz549cfjw4a7/quJi9Pb2xsDAgG1tkZxHF++hhx6Kp59+OnWMtlKtVuNtb3tb/Pvf/04dpW1YZ4s3NDQUp06dinq93vQysiyL3t7ea3+vVCqxdevWGBkZWY6ILU/BSnJHjhyJAwcOpI4BAAAAwDL69re/HUNDQ9d9a7kTdfb9ubS8//73v/GjH/0odQwAAAAAltG+ffvi3e9+d+oYq0LBSlL/+c9/lnQLOgAAAACtZf/+/TE8PJw6xqpRsJKUBxAAAAAAtKb169dHURTRaDSiKIoYHx+/4c90W7kaoWAlsXXr1qWOAAAAAMAsNm3aFI899lhEROR5Hg8//PC88+/bt6/rytWIiM4eYZaWt2PHjti4cWPqGAAAAAC8yYkTJ+KJJ56IPM9jdHR0zvmyLIuRkZHYuXPnKqZrHVlRFEXqEHS3n/3sZ3H48OHUMQAAAACYxfvf//74+te/Hr/+9a/jlVdeiVqtdm1af39/bN++PbZt25YwYVoKVlrCN7/5zThx4kTqGAAAAADM4n3ve1+MjIxERESpVIqenp7IsixKJV+QtwZIJs/zaDQa0Wg04hvf+Ea85z3vSR0JAAAAgFn87W9/i+985ztRLpcjy7IoikK5epU7WEkiz/OY2fR+85vfxB/+8IcYHx+Ps2fPJk4GAAAAwFy2bNkS3/rWt1LHaCk9qQPQfd5Yrh46dCgOHjyYOBEAAAAAC3Hy5Mk4ffp0bN68OXWUlqFgZVUpVwEAAABaz9DQUNx+++1RFEVkWRavvfZaFEURtVrt2lirtVot7rrrLuXqmyhYWTXKVQAAAIDW87nPfS4++clPXnt4VZ7nbxlfdWYab2WtsOxmitQ8zyPP88iyLBqNxrXphw4dil/96lcJEwIAAAAQEbF3797Ys2ePAnUJrDWWzcWLF+P8+fNx9uzZuHTp0rVitaen59qdq88991wcPXo0cVIAAAAAlKvLw5pjWRw5ciR++tOfxpUrV1JHAQAAAOAGlKvLx9pjyY4ePRoHDhxIHQMAAACABXjwwQeVq8vIGqRpeZ7HlStX4qmnnkodBQCaUiqVIs/z1DEAAGDFrFu3LgYHByMiYu3atXHPPffE8PCwcnUZWYs0rVQqRaVSibGxsdRRAKApylUAADrdxYsXY9euXfHFL37xutezLEuUqPMoWGlanucxOjoaU1NTqaMAAAAAMIdnnnkmzp07F/fee2+USqUYHByMoaGh1LE6RlbMPN4dmvT444/Hyy+/nDoGAAAAAAtQrVbjy1/+cuzatSt1lI5QSh2A9nfXXXeljgAAAADAApVKpfjoRz+aOkbHULCyYJOTk3H58uUYGxuLc+fOxejoaPzvf/+LT3ziE7Fu3brU8aCtVavVGBgYSB0DAACALnDPPfdEURTRaDQ8l2AZGCKABTl27Fj84Ac/iMnJybDJAAAAALSvLMvi4x//eDzyyCOpo3QEd7CyIL/73e9iYmJCuQoAAADQ5oqiiCNHjsTo6GjqKB2hJ3UAWlue51EURWzevDmOHTuWOg4AAAAATRgaGopS6fV7LQcGBmL37t0xODiYOFVnULAyr5kdDwAAAID2U61W43vf+15Uq9WoVCqxZs2a1JE6joKVBbl06VLqCAAAAAAsQq1Wi+9///tRrVajXC5HtVpNHakjKVhZEGNyAAAAALSuPXv2xG233RZZlkW5XI6BgYHYunVr9PT0KFdXmIKVec2Mwbp37944depUXLhwIYqiiHq9njoaAAAAAFdt27Yt3vve90a5XI5KpXLt9VKpFD09KsCVlBUeC89Vp0+fjmPHjsXf//73OHv2bExOTkbE6ztiuVy+bt56vR55nse5c+dSRAUAAADgDWq1Wtx8883X/r5mzZrYuXNn3H///QlTdQf1NRER8fvf/z5++MMfpo4BAAAAQBPGx8djfHz8utf+8Y9/xEc+8pG45ZZbPMh8BSlYiRdeeEG5CgAAANBhvvrVr8bGjRtTx+h4CtYuNjO+6jPPPJM6CgAAAADLaP/+/TE8PJw6RldQsHaxmVvDX3nllcRJAAAAAFguytXVZfAFolarpY4AAAAAwDLYt2+fcnWVKViJrVu3po4AAAAAwBKNjIzEzp07U8foOoYI6DLPP/98/OIXv4iXXnop6vV66jgAAAAALFK5XI7bb7/92t8HBgZi9+7dcffddydM1b0UrF3k2WefjSeffDJ1DAAAAACWoNFoxGc/+9n40Ic+lDoKYYiArvGnP/1JuQoAAADQISYmJlJH4Cp3sHa4PM9jYmIifvzjH6eOAgAAAMAyyLLMM3VaiDtYO1ypVIpyuRyXLl1KHQUAAACAZTA4OBi1Wi11DK5SsHaJoihSRwAAAABgidavXx9f+9rXor+/P3UUrjJEQIfL8zyyLItKpRLT09Op4wAAAAAwh97e3iiKIrIsi2q1GuVyOcrlcvT19cW2bdviYx/7WGzevDnWrVuXOipvoGDtcKVSKXp7e2P37t3x29/+NqampiLP8yiKIsrlckSE4hUAAACgBTz44INx3333RZZlUSr54nm7yArfHe86MwVrxOtDBxw4cCD++Mc/Jk4FAAAA0N0qlcp1X/3v7e2N7du3x8MPP5wwFTfiDtYuNPM/IHmeR0TEpk2bUsYBAAAAIF7/lvH58+eve+3cuXNx//33629amIK1i80UrVu2bEmcBAAAAIDZPProo8rVFmcwByLLstQRAAAAAHiT/fv3x/DwcOoY3ICClahWq6kjAAAAAPAGytX2oWAlBgcHU0cAAAAA4Kp9+/YpV9uIMViJnh6bAQAAAEArGBkZiQ9+8IOpY7AIWVEUReoQpPfcc8/FX/7yl7h8+XK89tprERGxZs2aWeedbfp8P7MS0240z0I+w8y0N/55KbmW8lmWe5lLWXcr+f6rvZ00u96b/QwrtdzV/owLWe5887TSv8mN9vWV+ByttL+v9rFspbKuVN5mzgUptqml/OxKnSebXXarTFvItjzXfK12/my392v2+qvZvK30GZeyXy1l2o3mWe7z60qdl5ead7mnpXjPVOfolci12vvmUt5zrvmWci6Zb1q7bR+pruGWc5krdUxr5nP09/fHjh07YseOHbP+DK1LwQoAAAAA0CRjsAIAAAAANEnBCgAAAADQJAUrAAAAAECTFKwAAAAAAE1SsAIAAAAANEnBCgAAAADQJAUrAAAAAECTFKwAAAAAAE1SsAIAAAAANEnBCgAAAADQJAUrAAAAAECTFKwAAAAAAE1SsAIAAAAANEnBCgAAAADQJAUrAAAAAECTFKwAAAAAAE1SsAIAAAAANEnBCgAAAADQJAUrAAAAAECTFKwAAAAAAE1SsAIAAAAANOn/ALAjXxq6a7tvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  24.52  24.58  23.68  23.71\n",
      "1    2010-01-05  23.75  23.90  22.75  23.30\n",
      "2    2010-01-06  23.25  23.25  22.72  22.90\n",
      "3    2010-01-07  22.90  23.05  22.40  22.65\n",
      "4    2010-01-08  22.50  22.75  22.35  22.60\n",
      "...         ...    ...    ...    ...    ...\n",
      "3564 2024-12-19  11.59  11.64  11.54  11.59\n",
      "3565 2024-12-20  11.59  11.70  11.58  11.62\n",
      "3566 2024-12-23  11.64  11.84  11.64  11.73\n",
      "3567 2024-12-24  11.72  11.87  11.72  11.86\n",
      "3568 2024-12-25  11.86  12.02  11.84  11.92\n",
      "\n",
      "[3569 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3569 entries, 0 to 3568\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3569 non-null   datetime64[ns]\n",
      " 1   open        3569 non-null   float64       \n",
      " 2   high        3569 non-null   float64       \n",
      " 3   low         3569 non-null   float64       \n",
      " 4   close       3569 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 139.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>24.52</td>\n",
       "      <td>24.58</td>\n",
       "      <td>23.68</td>\n",
       "      <td>23.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.90</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.72</td>\n",
       "      <td>22.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.05</td>\n",
       "      <td>22.40</td>\n",
       "      <td>22.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>22.35</td>\n",
       "      <td>22.60</td>\n",
       "      <td>23.032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.580</td>\n",
       "      <td>11.660</td>\n",
       "      <td>11.544333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.592</td>\n",
       "      <td>11.656</td>\n",
       "      <td>11.541000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.73</td>\n",
       "      <td>11.624</td>\n",
       "      <td>11.662</td>\n",
       "      <td>11.545333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.87</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.690</td>\n",
       "      <td>11.669</td>\n",
       "      <td>11.556000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>11.86</td>\n",
       "      <td>12.02</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.92</td>\n",
       "      <td>11.744</td>\n",
       "      <td>11.688</td>\n",
       "      <td>11.566333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3569 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-01-04  24.52  24.58  23.68  23.71     NaN     NaN        NaN\n",
       "1    2010-01-05  23.75  23.90  22.75  23.30     NaN     NaN        NaN\n",
       "2    2010-01-06  23.25  23.25  22.72  22.90     NaN     NaN        NaN\n",
       "3    2010-01-07  22.90  23.05  22.40  22.65     NaN     NaN        NaN\n",
       "4    2010-01-08  22.50  22.75  22.35  22.60  23.032     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3564 2024-12-19  11.59  11.64  11.54  11.59  11.580  11.660  11.544333\n",
       "3565 2024-12-20  11.59  11.70  11.58  11.62  11.592  11.656  11.541000\n",
       "3566 2024-12-23  11.64  11.84  11.64  11.73  11.624  11.662  11.545333\n",
       "3567 2024-12-24  11.72  11.87  11.72  11.86  11.690  11.669  11.556000\n",
       "3568 2024-12-25  11.86  12.02  11.84  11.92  11.744  11.688  11.566333\n",
       "\n",
       "[3569 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          11.86  12.02\n",
       "1          11.72  11.87\n",
       "2          11.64  11.84\n",
       "3          11.59  11.70\n",
       "4          11.59  11.64\n",
       "...          ...    ...\n",
       "3564       22.50  22.75\n",
       "3565       22.90  23.05\n",
       "3566       23.25  23.25\n",
       "3567       23.75  23.90\n",
       "3568       24.52  24.58\n",
       "\n",
       "[3569 rows x 2 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23.71],\n",
       "       [23.3 ],\n",
       "       [22.9 ],\n",
       "       ...,\n",
       "       [11.73],\n",
       "       [11.86],\n",
       "       [11.92]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>24.52</td>\n",
       "      <td>24.58</td>\n",
       "      <td>23.68</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>23.75</td>\n",
       "      <td>23.90</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>23.25</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.72</td>\n",
       "      <td>22.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>22.90</td>\n",
       "      <td>23.05</td>\n",
       "      <td>22.40</td>\n",
       "      <td>22.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>22.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>22.35</td>\n",
       "      <td>22.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>11.59</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>11.59</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>11.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>11.72</td>\n",
       "      <td>11.87</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>11.86</td>\n",
       "      <td>12.02</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3569 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  24.52  24.58  23.68  23.71\n",
       "14614.0  23.75  23.90  22.75  23.30\n",
       "14615.0  23.25  23.25  22.72  22.90\n",
       "14616.0  22.90  23.05  22.40  22.65\n",
       "14617.0  22.50  22.75  22.35  22.60\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  11.59  11.64  11.54  11.59\n",
       "20077.0  11.59  11.70  11.58  11.62\n",
       "20080.0  11.64  11.84  11.64  11.73\n",
       "20081.0  11.72  11.87  11.72  11.86\n",
       "20082.0  11.86  12.02  11.84  11.92\n",
       "\n",
       "[3569 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>24.52</td>\n",
       "      <td>24.58</td>\n",
       "      <td>23.68</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.90</td>\n",
       "      <td>22.75</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.25</td>\n",
       "      <td>22.72</td>\n",
       "      <td>22.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.05</td>\n",
       "      <td>22.40</td>\n",
       "      <td>22.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.75</td>\n",
       "      <td>22.35</td>\n",
       "      <td>22.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.54</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.58</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.87</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>11.86</td>\n",
       "      <td>12.02</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3569 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  24.52  24.58  23.68  23.71\n",
       "1     14614.0  23.75  23.90  22.75  23.30\n",
       "2     14615.0  23.25  23.25  22.72  22.90\n",
       "3     14616.0  22.90  23.05  22.40  22.65\n",
       "4     14617.0  22.50  22.75  22.35  22.60\n",
       "...       ...    ...    ...    ...    ...\n",
       "3564  20076.0  11.59  11.64  11.54  11.59\n",
       "3565  20077.0  11.59  11.70  11.58  11.62\n",
       "3566  20080.0  11.64  11.84  11.64  11.73\n",
       "3567  20081.0  11.72  11.87  11.72  11.86\n",
       "3568  20082.0  11.86  12.02  11.84  11.92\n",
       "\n",
       "[3569 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151e79cf710>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.1287 - val_loss: 0.0164\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0191 - val_loss: 0.0017\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0028 - val_loss: 5.2478e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0019 - val_loss: 3.9045e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0017 - val_loss: 3.8833e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0017 - val_loss: 4.1814e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0016 - val_loss: 4.3775e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0016 - val_loss: 4.2953e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0015 - val_loss: 4.1487e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0015 - val_loss: 4.0082e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0014 - val_loss: 3.8789e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0014 - val_loss: 3.6675e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 3.4704e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 3.3096e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0012 - val_loss: 3.3006e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 3.3062e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 3.3107e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 3.2713e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 3.2041e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 3.0722e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 2.9976e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 2.8877e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.7924e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.7613e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.7125e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.6363e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.6040e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 2.5654e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.9189e-04 - val_loss: 2.5211e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.7891e-04 - val_loss: 2.4702e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.06073\n",
      "均方根误差: 0.24644\n",
      "平均绝对误差: 0.17737\n",
      "R2: 0.89754\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
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